pytorch_pfn_extras.training.extensions.PlotReport¶
- class pytorch_pfn_extras.training.extensions.PlotReport(y_keys, x_key='iteration', trigger=(1, 'epoch'), postprocess=None, filename='plot.png', marker='x', grid=True)¶
An extension to output plots.
This extension accumulates the observations of the manager to
DictSummary
at a regular interval specified by a supplied trigger, and plot a graph with using them.There are two triggers to handle this extension. One is the trigger to invoke this extension, which is used to handle the timing of accumulating the results. It is set to
1, 'iteration'
by default. The other is the trigger to determine when to emit the result. When this trigger returns True, this extension appends the summary of accumulated values to the list of past summaries, and writes the list to the log file. Then, this extension makes a new fresh summary object which is used until the next time that the trigger fires.It also adds
'epoch'
and'iteration'
entries to each result dictionary, which are the epoch and iteration counts at the output.Warning
If your environment needs to specify a backend of matplotlib explicitly, please call
matplotlib.use
before callingmanager.run_iteration
. For example:import matplotlib matplotlib.use('Agg') manager.extend( extensions.PlotReport(['main/loss', 'validation/main/loss'], 'epoch', filename='loss.png')) with manager.run_iteration(): pass
Then, once one of instances of this extension is called,
matplotlib.use
will have no effect.For the details, please see here: https://matplotlib.org/faq/usage_faq.html#what-is-a-backend
- Parameters
y_keys (iterable of strs) – Keys of values regarded as y. If this is
None
, nothing is output to the graph.x_key (str) – Keys of values regarded as x. The default value is ‘iteration’.
trigger – Trigger that decides when to aggregate the result and output the values. This is distinct from the trigger of this extension itself. If it is a tuple in the form
<int>, 'epoch'
or<int>, 'iteration'
, it is passed toIntervalTrigger
.postprocess – Callback to postprocess the result dictionaries. Figure object, Axes object, and all plot data are passed to this callback in this order. This callback can modify the figure.
filename (str) – Name of the figure file under the output directory. It can be a format string. For historical reasons
file_name
is also accepted as an alias of this argument.marker (str) – The marker used to plot the graph. Default is
'x'
. IfNone
is given, it draws with no markers.grid (bool) – If
True
, set the axis grid on. The default value isTrue
.writer (writer object, optional) – must be callable. object to dump the log to. If specified, it needs to have a correct savefun defined. The writer can override the save location in the
pytorch_pfn_extras.training.ExtensionsManager
object
- __init__(y_keys, x_key='iteration', trigger=(1, 'epoch'), postprocess=None, filename=None, marker='x', grid=True, **kwargs)¶
Methods
__init__
(y_keys[, x_key, trigger, …])available
()finalize
()Finalizes the extension.
initialize
(manager)Initializes up the manager state.
load_state_dict
(to_load)on_error
(manager, exc, tb)Handles the error raised during training before finalization.
state_dict
()Serializes the extension state.
Attributes
default_name
Default name of the extension.
is_async
name
needs_model_state
priority
trigger