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logger.py
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logger.py
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import os
import torch
import pickle
import imageio
import torchvision
import numpy as np
import utils
class Logger(object):
def __init__(self,
log_dir,
img_dir,
monitoring=None,
monitoring_dir=None):
self.stats = dict()
self.log_dir = log_dir
self.img_dir = img_dir
utils.cond_mkdir(self.log_dir)
utils.cond_mkdir(self.img_dir)
if not (monitoring is None or monitoring == 'none'):
self.setup_monitoring(monitoring, monitoring_dir)
else:
self.monitoring = None
self.monitoring_dir = None
def setup_monitoring(self, monitoring, monitoring_dir):
self.monitoring = monitoring
self.monitoring_dir = monitoring_dir
if monitoring == 'telemetry':
import telemetry
self.tm = telemetry.ApplicationTelemetry()
if self.tm.get_status() == 0:
print('Telemetry successfully connected.')
elif monitoring == 'tensorboard':
import tensorboardX
self.tb = tensorboardX.SummaryWriter(self.monitoring_dir)
else:
raise NotImplementedError('Monitoring tool "%s" not supported!'
% monitoring)
def add(self, category, k, v, it):
if category not in self.stats:
self.stats[category] = {}
if k not in self.stats[category]:
self.stats[category][k] = []
self.stats[category][k].append((it, v))
k_name = '%s/%s' % (category, k)
if self.monitoring == 'telemetry':
self.tm.metric_push_async({
'metric': k_name, 'value': v, 'it': it
})
elif self.monitoring == 'tensorboard':
self.tb.add_scalar(k_name, v, it)
def add_vector(self, category, k, vec, it):
if category not in self.stats:
self.stats[category] = {}
if k not in self.stats[category]:
self.stats[category][k] = []
if isinstance(vec, torch.Tensor):
vec = vec.data.clone().cpu().numpy()
self.stats[category][k].append((it, vec))
def add_imgs(self, imgs, class_name, it):
outdir = os.path.join(self.img_dir, class_name)
if not os.path.exists(outdir):
os.makedirs(outdir)
outfile = os.path.join(outdir, '%08d.png' % it)
# imgs = imgs / 2 + 0.5
imgs = torchvision.utils.make_grid(imgs)
torchvision.utils.save_image(imgs.clone(), outfile, nrow=8)
if self.monitoring == 'tensorboard':
self.tb.add_image(class_name, imgs, global_step=it)
def add_figure(self, fig, class_name, it, save_img=True):
if save_img:
outdir = os.path.join(self.img_dir, class_name)
if not os.path.exists(outdir):
os.makedirs(outdir)
outfile = os.path.join(outdir, '%08d.png' % it)
image_hwc = utils.figure_to_image(fig)
imageio.imwrite(outfile, image_hwc)
if self.monitoring == 'tensorboard':
if len(image_hwc.shape) == 3:
image_hwc = np.array(image_hwc[None, ...])
self.tb.add_images(class_name, torch.from_numpy(image_hwc), dataformats='NHWC')
else:
if self.monitoring == 'tensorboard':
self.tb.add_figure(class_name, fig, it)
def add_module_param(self, module_name, module, it):
if self.monitoring == 'tensorboard':
for name, param in module.named_parameters():
self.tb.add_histogram("{}/{}".format(module_name, name), param.detach(), it)
def get_last(self, category, k, default=0.):
if category not in self.stats:
return default
elif k not in self.stats[category]:
return default
else:
return self.stats[category][k][-1][1]
def save_stats(self, filename):
filename = os.path.join(self.log_dir, filename)
with open(filename, 'wb') as f:
pickle.dump(self.stats, f)
def load_stats(self, filename):
filename = os.path.join(self.log_dir, filename)
if not os.path.exists(filename):
# print('=> File "%s" does not exist, will create new after calling save_stats()' % filename)
return
try:
with open(filename, 'rb') as f:
self.stats = pickle.load(f)
print("=> Load file: {}".format(filename))
except EOFError:
print('Warning: log file corrupted!')