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import os
import json
import time
import torch
# import matplotlib.pyplot as plt
import collections
class TotalAverage():
def __init__(self):
self.reset()
def reset(self):
self.last_value = 0.
self.mass = 0.
self.sum = 0.
def update(self, value, mass=1):
self.last_value = value
self.mass += mass
self.sum += value * mass
def get(self):
return self.sum / self.mass
class MovingAverage():
def __init__(self, inertia=0.9):
self.inertia = inertia
self.reset()
self.last_value = None
def reset(self):
self.last_value = None
self.average = None
def update(self, value, mass=1):
self.last_value = value
if self.average is None:
self.average = value
else:
self.average = self.inertia * self.average + (1 - self.inertia) * value
def get(self):
return self.average
class MetricsTrace:
def __init__(self):
self.data = {}
self.reset()
def reset(self):
self.data = {}
def append(self, dataset, metric):
if dataset not in self.data:
self.data[dataset] = []
self.data[dataset].append(metric.get_data_dict())
def load(self, path):
"""Load the metrics trace from the specified JSON file."""
with open(path, 'r') as f:
self.data = json.load(f)
def save(self, path):
"""Save the metrics trace to the specified JSON file."""
if path is None:
return
os.makedirs(os.path.dirname(path), exist_ok=True)
with open(path, 'w') as f:
json.dump(self.data, f, indent=2)
def plot(self, pdf_path=None):
"""Plots and optionally save as PDF the metrics trace."""
plot_metrics(self.data, pdf_path=pdf_path)
def get(self):
return self.data
def __str__(self):
pass
class Metrics():
def __init__(self):
self.iteration_time = MovingAverage(inertia=0.9)
self.now = time.time()
def update(self, prediction=None, ground_truth=None):
self.iteration_time.update(time.time() - self.now)
self.now = time.time()
def get_data_dict(self):
return {"objective" : self.objective.get(), "iteration_time" : self.iteration_time.get()}
class StandardMetrics(Metrics):
def __init__(self, m=None):
super(StandardMetrics, self).__init__()
self.metrics = m or {}
self.speed = MovingAverage(inertia=0.9)
def update(self, metric_dict, mass=1):
super(StandardMetrics, self).update()
for metric, val in metric_dict.items():
if torch.is_tensor(val):
val = val.item()
if metric not in self.metrics:
if 'moving_average' in metric:
try:
p = float(metric.split('moving_average')[-1].split('_')[-1])
except:
p = 0.9
self.metrics[metric] = MovingAverage(p)
else:
self.metrics[metric] = TotalAverage()
self.metrics[metric].update(val, mass)
self.speed.update(mass / self.iteration_time.last_value)
def get_data_dict(self):
data_dict = {k: v.get() for k,v in self.metrics.items()}
data_dict['speed'] = self.speed.get()
return data_dict
def __str__(self):
pstr = '%7.1fHz\t' %self.speed.get()
pstr += '\t'.join(['%s: %6.5f' %(k,v.get()) for k,v in self.metrics.items()])
return pstr
def plot_metrics(stats, pdf_path=None, fig=1, datasets=None, metrics=None):
"""Plot metrics. `stats` should be a dictionary of type
stats[dataset][t][metric][i]
where dataset is the dataset name (e.g. `train` or `val`), t is an iteration number,
metric is the name of a metric (e.g. `loss` or `top1`), and i is a loss dimension.
Alternatively, if a loss has a single dimension, `stats[dataset][t][metric]` can
be a scalar.
The supported options are:
- pdf_file: path to a PDF file to store the figure (default: None)
- fig: MatPlotLib figure index (default: 1)
- datasets: list of dataset names to plot (default: None)
- metrics: list of metrics to plot (default: None)
"""
plt.figure(fig)
plt.clf()
linestyles = ['-', '--', '-.', ':']
datasets = list(stats.keys()) if datasets is None else datasets
# Filter out empty datasets
datasets = [d for d in datasets if len(stats[d]) > 0]
duration = len(stats[datasets[0]])
metrics = list(stats[datasets[0]][0].keys()) if metrics is None else metrics
for m, metric in enumerate(metrics):
plt.subplot(len(metrics),1,m+1)
legend_content = []
for d, dataset in enumerate(datasets):
ls = linestyles[d % len(linestyles)]
if isinstance(stats[dataset][0][metric], collections.Iterable):
metric_dimension = len(stats[dataset][0][metric])
for sl in range(metric_dimension):
x = [stats[dataset][t][metric][sl] for t in range(duration)]
plt.plot(x, linestyle=ls)
name = f'{dataset} {metric}[{sl}]'
legend_content.append(name)
else:
x = [stats[dataset][t][metric] for t in range(duration)]
plt.plot(x, linestyle=ls)
name = f'{dataset} {metric}'
legend_content.append(name)
plt.legend(legend_content, loc=(1.04,0))
plt.grid(True)
if pdf_path is not None:
plt.savefig(pdf_path, format='pdf', bbox_inches='tight')
plt.draw()
plt.pause(0.0001)
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