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# coding: utf-8
"""Callback functions that can be used to track various status during epoch."""
from __future__ import absolute_import
import logging
import math
import time
from .model import save_checkpoint
[docs]def module_checkpoint(mod, prefix, period=1, save_optimizer_states=False):
"""Callback to checkpoint Module to prefix every epoch.
Parameters
----------
mod : subclass of BaseModule
The module to checkpoint.
prefix : str
The file prefix for this checkpoint.
period : int
How many epochs to wait before checkpointing. Defaults to 1.
save_optimizer_states : bool
Indicates whether or not to save optimizer states for continued training.
Returns
-------
callback : function
The callback function that can be passed as iter_end_callback to fit.
"""
period = int(max(1, period))
# pylint: disable=unused-argument
def _callback(iter_no, sym=None, arg=None, aux=None):
"""The checkpoint function."""
if (iter_no + 1) % period == 0:
mod.save_checkpoint(prefix, iter_no + 1, save_optimizer_states)
return _callback
[docs]def do_checkpoint(prefix, period=1):
"""A callback that saves a model checkpoint every few epochs.
Each checkpoint is made up of a couple of binary files: a model description file and a
parameters (weights and biases) file. The model description file is named
`prefix`--symbol.json and the parameters file is named `prefix`-`epoch_number`.params
Parameters
----------
prefix : str
Prefix for the checkpoint filenames.
period : int, optional
Interval (number of epochs) between checkpoints. Default `period` is 1.
Returns
-------
callback : function
A callback function that can be passed as `epoch_end_callback` to fit.
Example
-------
>>> module.fit(iterator, num_epoch=n_epoch,
... epoch_end_callback = mx.callback.do_checkpoint("mymodel", 1))
Start training with [cpu(0)]
Epoch[0] Resetting Data Iterator
Epoch[0] Time cost=0.100
Saved checkpoint to "mymodel-0001.params"
Epoch[1] Resetting Data Iterator
Epoch[1] Time cost=0.060
Saved checkpoint to "mymodel-0002.params"
"""
period = int(max(1, period))
def _callback(iter_no, sym, arg, aux):
"""The checkpoint function."""
if (iter_no + 1) % period == 0:
save_checkpoint(prefix, iter_no + 1, sym, arg, aux)
return _callback
[docs]def log_train_metric(period, auto_reset=False):
"""Callback to log the training evaluation result every period.
Parameters
----------
period : int
The number of batch to log the training evaluation metric.
auto_reset : bool
Reset the metric after each log.
Returns
-------
callback : function
The callback function that can be passed as iter_epoch_callback to fit.
"""
def _callback(param):
"""The checkpoint function."""
if param.nbatch % period == 0 and param.eval_metric is not None:
name_value = param.eval_metric.get_name_value()
for name, value in name_value:
logging.info('Iter[%d] Batch[%d] Train-%s=%f',
param.epoch, param.nbatch, name, value)
if auto_reset:
param.eval_metric.reset()
return _callback
[docs]class Speedometer(object):
"""Logs training speed and evaluation metrics periodically.
Parameters
----------
batch_size: int
Batch size of data.
frequent: int
Specifies how frequently training speed and evaluation metrics
must be logged. Default behavior is to log once every 50 batches.
auto_reset : bool
Reset the evaluation metrics after each log.
Example
-------
>>> # Print training speed and evaluation metrics every ten batches. Batch size is one.
>>> module.fit(iterator, num_epoch=n_epoch,
... batch_end_callback=mx.callback.Speedometer(1, 10))
Epoch[0] Batch [10] Speed: 1910.41 samples/sec Train-accuracy=0.200000
Epoch[0] Batch [20] Speed: 1764.83 samples/sec Train-accuracy=0.400000
Epoch[0] Batch [30] Speed: 1740.59 samples/sec Train-accuracy=0.500000
"""
def __init__(self, batch_size, frequent=50, auto_reset=True):
self.batch_size = batch_size
self.frequent = frequent
self.init = False
self.tic = 0
self.last_count = 0
self.auto_reset = auto_reset
def __call__(self, param):
"""Callback to Show speed."""
count = param.nbatch
if self.last_count > count:
self.init = False
self.last_count = count
if self.init:
if count % self.frequent == 0:
speed = self.frequent * self.batch_size / (time.time() - self.tic)
if param.eval_metric is not None:
name_value = param.eval_metric.get_name_value()
if self.auto_reset:
param.eval_metric.reset()
msg = 'Epoch[%d] Batch [%d]\tSpeed: %.2f samples/sec'
msg += '\t%s=%f'*len(name_value)
logging.info(msg, param.epoch, count, speed, *sum(name_value, ()))
else:
logging.info("Iter[%d] Batch [%d]\tSpeed: %.2f samples/sec",
param.epoch, count, speed)
self.tic = time.time()
else:
self.init = True
self.tic = time.time()
[docs]class ProgressBar(object):
"""Displays a progress bar, indicating the percentage of batches processed within each epoch.
Parameters
----------
total: int
total number of batches per epoch
length: int
number of chars to define maximum length of progress bar
Examples
--------
>>> progress_bar = mx.callback.ProgressBar(total=2)
>>> mod.fit(data, num_epoch=5, batch_end_callback=progress_bar)
[========--------] 50.0%
[================] 100.0%
"""
def __init__(self, total, length=80):
self.bar_len = length
self.total = total
def __call__(self, param):
"""Callback to Show progress bar."""
count = param.nbatch
filled_len = int(round(self.bar_len * count / float(self.total)))
percents = math.ceil(100.0 * count / float(self.total))
prog_bar = '=' * filled_len + '-' * (self.bar_len - filled_len)
logging.info('[%s] %s%s\r', prog_bar, percents, '%')
[docs]class LogValidationMetricsCallback(object):
"""Just logs the eval metrics at the end of an epoch."""
def __call__(self, param):
if not param.eval_metric:
return
name_value = param.eval_metric.get_name_value()
for name, value in name_value:
logging.info('Epoch[%d] Validation-%s=%f', param.epoch, name, value)
