DeepLearningExamples/PyTorch/Classification/ConvNets/image_classification/logger.py

# Copyright (c) 2018-2019, NVIDIA CORPORATION
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from collections import OrderedDict
import dllogger
import numpy as np


def format_step(step):
    if isinstance(step, str):
        return step
    s = ""
    if len(step) > 0:
        s += "Epoch: {} ".format(step[0])
    if len(step) > 1:
        s += "Iteration: {} ".format(step[1])
    if len(step) > 2:
        s += "Validation Iteration: {} ".format(step[2])
    if len(step) == 0:
        s = "Summary:"
    return s


PERF_METER = lambda: Meter(AverageMeter(), AverageMeter(), AverageMeter())
LOSS_METER = lambda: Meter(AverageMeter(), AverageMeter(), MinMeter())
ACC_METER = lambda: Meter(AverageMeter(), AverageMeter(), MaxMeter())
LR_METER = lambda: Meter(LastMeter(), LastMeter(), LastMeter())

LAT_100 = lambda: Meter(QuantileMeter(1), QuantileMeter(1), QuantileMeter(1))
LAT_99 = lambda: Meter(QuantileMeter(0.99), QuantileMeter(0.99), QuantileMeter(0.99))
LAT_95 = lambda: Meter(QuantileMeter(0.95), QuantileMeter(0.95), QuantileMeter(0.95))


class Meter(object):
    def __init__(self, iteration_aggregator, epoch_aggregator, run_aggregator):
        self.run_aggregator = run_aggregator
        self.epoch_aggregator = epoch_aggregator
        self.iteration_aggregator = iteration_aggregator

    def record(self, val, n=1):
        self.iteration_aggregator.record(val, n=n)

    def get_iteration(self):
        v, n = self.iteration_aggregator.get_val()
        return v

    def reset_iteration(self):
        v, n = self.iteration_aggregator.get_data()
        self.iteration_aggregator.reset()
        if v is not None:
            self.epoch_aggregator.record(v, n=n)

    def get_epoch(self):
        v, n = self.epoch_aggregator.get_val()
        return v

    def reset_epoch(self):
        v, n = self.epoch_aggregator.get_data()
        self.epoch_aggregator.reset()
        if v is not None:
            self.run_aggregator.record(v, n=n)

    def get_run(self):
        v, n = self.run_aggregator.get_val()
        return v

    def reset_run(self):
        self.run_aggregator.reset()


class QuantileMeter(object):
    def __init__(self, q):
        self.q = q
        self.reset()

    def reset(self):
        self.vals = []
        self.n = 0

    def record(self, val, n=1):
        if isinstance(val, list):
            self.vals += val
            self.n += len(val)
        else:
            self.vals += [val] * n
            self.n += n

    def get_val(self):
        if not self.vals:
            return None, self.n
        return np.quantile(self.vals, self.q, interpolation="nearest"), self.n

    def get_data(self):
        return self.vals, self.n


class MaxMeter(object):
    def __init__(self):
        self.reset()

    def reset(self):
        self.max = None
        self.n = 0

    def record(self, val, n=1):
        if self.max is None:
            self.max = val
        else:
            self.max = max(self.max, val)
        self.n = n

    def get_val(self):
        return self.max, self.n

    def get_data(self):
        return self.max, self.n


class MinMeter(object):
    def __init__(self):
        self.reset()

    def reset(self):
        self.min = None
        self.n = 0

    def record(self, val, n=1):
        if self.min is None:
            self.min = val
        else:
            self.min = max(self.min, val)
        self.n = n

    def get_val(self):
        return self.min, self.n

    def get_data(self):
        return self.min, self.n


class LastMeter(object):
    def __init__(self):
        self.reset()

    def reset(self):
        self.last = None
        self.n = 0

    def record(self, val, n=1):
        self.last = val
        self.n = n

    def get_val(self):
        return self.last, self.n

    def get_data(self):
        return self.last, self.n


class AverageMeter(object):
    def __init__(self):
        self.reset()

    def reset(self):
        self.n = 0
        self.val = 0

    def record(self, val, n=1):
        self.n += n
        self.val += val * n

    def get_val(self):
        if self.n == 0:
            return None, 0
        return self.val / self.n, self.n

    def get_data(self):
        if self.n == 0:
            return None, 0
        return self.val / self.n, self.n


class Logger(object):
    def __init__(self, print_interval, backends, start_epoch=-1, verbose=False):
        self.epoch = start_epoch
        self.iteration = -1
        self.val_iteration = -1
        self.metrics = OrderedDict()
        self.backends = backends
        self.print_interval = print_interval
        self.verbose = verbose
        dllogger.init(backends)

    def log_parameter(self, data, verbosity=0):
        dllogger.log(step="PARAMETER", data=data, verbosity=verbosity)

    def register_metric(self, metric_name, meter, verbosity=0, metadata={}):
        if self.verbose:
            print("Registering metric: {}".format(metric_name))
        self.metrics[metric_name] = {"meter": meter, "level": verbosity}
        dllogger.metadata(metric_name, metadata)

    def log_metric(self, metric_name, val, n=1):
        self.metrics[metric_name]["meter"].record(val, n=n)

    def start_iteration(self, val=False):
        if val:
            self.val_iteration += 1
        else:
            self.iteration += 1

    def end_iteration(self, val=False):
        it = self.val_iteration if val else self.iteration
        if it % self.print_interval == 0:
            metrics = {
                n: m for n, m in self.metrics.items() if n.startswith("val") == val
            }
            step = (
                (self.epoch, self.iteration)
                if not val
                else (self.epoch, self.iteration, self.val_iteration)
            )

            verbositys = {m["level"] for _, m in metrics.items()}
            for ll in verbositys:
                llm = {n: m for n, m in metrics.items() if m["level"] == ll}

                dllogger.log(
                    step=step,
                    data={n: m["meter"].get_iteration() for n, m in llm.items()},
                    verbosity=ll,
                )

            for n, m in metrics.items():
                m["meter"].reset_iteration()

            dllogger.flush()

    def start_epoch(self):
        self.epoch += 1
        self.iteration = 0
        self.val_iteration = 0

        for n, m in self.metrics.items():
            m["meter"].reset_epoch()

    def end_epoch(self):
        for n, m in self.metrics.items():
            m["meter"].reset_iteration()

        verbositys = {m["level"] for _, m in self.metrics.items()}
        for ll in verbositys:
            llm = {n: m for n, m in self.metrics.items() if m["level"] == ll}
            dllogger.log(
                step=(self.epoch,),
                data={n: m["meter"].get_epoch() for n, m in llm.items()},
            )

    def end(self):
        for n, m in self.metrics.items():
            m["meter"].reset_epoch()

        verbositys = {m["level"] for _, m in self.metrics.items()}
        for ll in verbositys:
            llm = {n: m for n, m in self.metrics.items() if m["level"] == ll}
            dllogger.log(
                step=tuple(), data={n: m["meter"].get_run() for n, m in llm.items()}
            )

        for n, m in self.metrics.items():
            m["meter"].reset_epoch()

        dllogger.flush()

    def iteration_generator_wrapper(self, gen, val=False):
        for g in gen:
            self.start_iteration(val=val)
            yield g
            self.end_iteration(val=val)

    def epoch_generator_wrapper(self, gen):
        for g in gen:
            self.start_epoch()
            yield g
            self.end_epoch()