312 lines
9 KiB
Python
312 lines
9 KiB
Python
# Copyright (c) 2018-2019, NVIDIA CORPORATION
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# Copyright (c) 2017- Facebook, Inc
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#
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# All rights reserved.
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#
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# Redistribution and use in source and binary forms, with or without
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# modification, are permitted provided that the following conditions are met:
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#
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# * Redistributions of source code must retain the above copyright notice, this
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# list of conditions and the following disclaimer.
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#
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# * Redistributions in binary form must reproduce the above copyright notice,
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# this list of conditions and the following disclaimer in the documentation
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# and/or other materials provided with the distribution.
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#
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# * Neither the name of the copyright holder nor the names of its
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# contributors may be used to endorse or promote products derived from
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# this software without specific prior written permission.
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#
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# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
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# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
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# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
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# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
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# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
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# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
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# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
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# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
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# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
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# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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from collections import OrderedDict
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import dllogger
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import numpy as np
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def format_step(step):
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if isinstance(step, str):
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return step
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s = ""
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if len(step) > 0:
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s += "Epoch: {} ".format(step[0])
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if len(step) > 1:
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s += "Iteration: {} ".format(step[1])
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if len(step) > 2:
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s += "Validation Iteration: {} ".format(step[2])
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if len(step) == 0:
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s = "Summary:"
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return s
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PERF_METER = lambda: Meter(AverageMeter(), AverageMeter(), AverageMeter())
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LOSS_METER = lambda: Meter(AverageMeter(), AverageMeter(), MinMeter())
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ACC_METER = lambda: Meter(AverageMeter(), AverageMeter(), MaxMeter())
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LR_METER = lambda: Meter(LastMeter(), LastMeter(), LastMeter())
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LAT_100 = lambda: Meter(QuantileMeter(1), QuantileMeter(1), QuantileMeter(1))
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LAT_99 = lambda: Meter(QuantileMeter(0.99), QuantileMeter(0.99), QuantileMeter(0.99))
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LAT_95 = lambda: Meter(QuantileMeter(0.95), QuantileMeter(0.95), QuantileMeter(0.95))
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class Meter(object):
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def __init__(self, iteration_aggregator, epoch_aggregator, run_aggregator):
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self.run_aggregator = run_aggregator
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self.epoch_aggregator = epoch_aggregator
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self.iteration_aggregator = iteration_aggregator
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def record(self, val, n=1):
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self.iteration_aggregator.record(val, n=n)
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def get_iteration(self):
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v, n = self.iteration_aggregator.get_val()
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return v
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def reset_iteration(self):
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v, n = self.iteration_aggregator.get_data()
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self.iteration_aggregator.reset()
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if v is not None:
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self.epoch_aggregator.record(v, n=n)
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def get_epoch(self):
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v, n = self.epoch_aggregator.get_val()
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return v
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def reset_epoch(self):
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v, n = self.epoch_aggregator.get_data()
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self.epoch_aggregator.reset()
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if v is not None:
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self.run_aggregator.record(v, n=n)
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def get_run(self):
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v, n = self.run_aggregator.get_val()
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return v
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def reset_run(self):
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self.run_aggregator.reset()
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class QuantileMeter(object):
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def __init__(self, q):
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self.q = q
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self.reset()
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def reset(self):
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self.vals = []
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self.n = 0
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def record(self, val, n=1):
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if isinstance(val, list):
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self.vals += val
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self.n += len(val)
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else:
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self.vals += [val] * n
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self.n += n
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def get_val(self):
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if not self.vals:
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return None, self.n
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return np.quantile(self.vals, self.q, interpolation="nearest"), self.n
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def get_data(self):
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return self.vals, self.n
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class MaxMeter(object):
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def __init__(self):
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self.reset()
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def reset(self):
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self.max = None
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self.n = 0
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def record(self, val, n=1):
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if self.max is None:
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self.max = val
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else:
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self.max = max(self.max, val)
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self.n = n
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def get_val(self):
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return self.max, self.n
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def get_data(self):
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return self.max, self.n
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class MinMeter(object):
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def __init__(self):
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self.reset()
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def reset(self):
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self.min = None
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self.n = 0
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def record(self, val, n=1):
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if self.min is None:
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self.min = val
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else:
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self.min = max(self.min, val)
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self.n = n
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def get_val(self):
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return self.min, self.n
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def get_data(self):
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return self.min, self.n
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class LastMeter(object):
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def __init__(self):
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self.reset()
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def reset(self):
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self.last = None
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self.n = 0
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def record(self, val, n=1):
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self.last = val
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self.n = n
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def get_val(self):
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return self.last, self.n
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def get_data(self):
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return self.last, self.n
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class AverageMeter(object):
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def __init__(self):
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self.reset()
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def reset(self):
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self.n = 0
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self.val = 0
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def record(self, val, n=1):
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self.n += n
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self.val += val * n
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def get_val(self):
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if self.n == 0:
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return None, 0
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return self.val / self.n, self.n
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def get_data(self):
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if self.n == 0:
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return None, 0
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return self.val / self.n, self.n
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class Logger(object):
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def __init__(self, print_interval, backends, start_epoch=-1, verbose=False):
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self.epoch = start_epoch
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self.iteration = -1
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self.val_iteration = -1
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self.metrics = OrderedDict()
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self.backends = backends
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self.print_interval = print_interval
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self.verbose = verbose
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dllogger.init(backends)
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def log_parameter(self, data, verbosity=0):
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dllogger.log(step="PARAMETER", data=data, verbosity=verbosity)
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def register_metric(self, metric_name, meter, verbosity=0, metadata={}):
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if self.verbose:
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print("Registering metric: {}".format(metric_name))
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self.metrics[metric_name] = {"meter": meter, "level": verbosity}
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dllogger.metadata(metric_name, metadata)
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def log_metric(self, metric_name, val, n=1):
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self.metrics[metric_name]["meter"].record(val, n=n)
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def start_iteration(self, val=False):
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if val:
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self.val_iteration += 1
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else:
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self.iteration += 1
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def end_iteration(self, val=False):
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it = self.val_iteration if val else self.iteration
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if it % self.print_interval == 0:
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metrics = {
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n: m for n, m in self.metrics.items() if n.startswith("val") == val
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}
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step = (
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(self.epoch, self.iteration)
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if not val
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else (self.epoch, self.iteration, self.val_iteration)
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)
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verbositys = {m["level"] for _, m in metrics.items()}
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for ll in verbositys:
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llm = {n: m for n, m in metrics.items() if m["level"] == ll}
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dllogger.log(
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step=step,
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data={n: m["meter"].get_iteration() for n, m in llm.items()},
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verbosity=ll,
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)
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for n, m in metrics.items():
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m["meter"].reset_iteration()
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dllogger.flush()
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def start_epoch(self):
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self.epoch += 1
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self.iteration = 0
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self.val_iteration = 0
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for n, m in self.metrics.items():
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m["meter"].reset_epoch()
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def end_epoch(self):
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for n, m in self.metrics.items():
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m["meter"].reset_iteration()
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verbositys = {m["level"] for _, m in self.metrics.items()}
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for ll in verbositys:
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llm = {n: m for n, m in self.metrics.items() if m["level"] == ll}
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dllogger.log(
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step=(self.epoch,),
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data={n: m["meter"].get_epoch() for n, m in llm.items()},
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)
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def end(self):
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for n, m in self.metrics.items():
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m["meter"].reset_epoch()
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verbositys = {m["level"] for _, m in self.metrics.items()}
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for ll in verbositys:
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llm = {n: m for n, m in self.metrics.items() if m["level"] == ll}
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dllogger.log(
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step=tuple(), data={n: m["meter"].get_run() for n, m in llm.items()}
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)
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for n, m in self.metrics.items():
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m["meter"].reset_epoch()
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dllogger.flush()
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def iteration_generator_wrapper(self, gen, val=False):
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for g in gen:
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self.start_iteration(val=val)
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yield g
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self.end_iteration(val=val)
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def epoch_generator_wrapper(self, gen):
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for g in gen:
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self.start_epoch()
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yield g
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self.end_epoch()
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