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Use logging for messages in ImageNet example #93

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Use logging for messages in ImageNet example #93

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57 changes: 33 additions & 24 deletions imagenet/main.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2,6 +2,7 @@
import os
import shutil
import time
import logging

import torch
import torch.nn as nn
Expand All @@ -14,6 +15,14 @@
import torchvision.models as models


logger = logging.getLogger("torch.examples.imagenet")
logger.setLevel(logging.DEBUG)
log_handler = logging.StreamHandler()
log_handler.setLevel(logging.DEBUG)
log_handler.setFormatter(logging.Formatter('=> %(message)s'))
logger.addHandler(log_handler)


model_names = sorted(name for name in models.__dict__
if name.islower() and not name.startswith("__")
and callable(models.__dict__[name]))
Expand Down Expand Up @@ -59,10 +68,10 @@ def main():

# create model
if args.pretrained:
print("=> using pre-trained model '{}'".format(args.arch))
logger.info("using pre-trained model '{}'".format(args.arch))
model = models.__dict__[args.arch](pretrained=True)
else:
print("=> creating model '{}'".format(args.arch))
logger.info("creating model '{}'".format(args.arch))
model = models.__dict__[args.arch]()

if args.arch.startswith('alexnet') or args.arch.startswith('vgg'):
Expand All @@ -74,15 +83,15 @@ def main():
# optionally resume from a checkpoint
if args.resume:
if os.path.isfile(args.resume):
print("=> loading checkpoint '{}'".format(args.resume))
logger.info("loading checkpoint '{}'".format(args.resume))
checkpoint = torch.load(args.resume)
args.start_epoch = checkpoint['epoch']
best_prec1 = checkpoint['best_prec1']
model.load_state_dict(checkpoint['state_dict'])
print("=> loaded checkpoint '{}' (epoch {})"
.format(args.evaluate, checkpoint['epoch']))
logger.info("loaded checkpoint '{}' (epoch {})"
.format(args.resume, checkpoint['epoch']))
else:
print("=> no checkpoint found at '{}'".format(args.resume))
logger.error("no checkpoint found at '{}'".format(args.resume))

cudnn.benchmark = True

Expand Down Expand Up @@ -182,14 +191,14 @@ def train(train_loader, model, criterion, optimizer, epoch):
end = time.time()

if i % args.print_freq == 0:
print('Epoch: [{0}][{1}/{2}]\t'
'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
'Data {data_time.val:.3f} ({data_time.avg:.3f})\t'
'Loss {loss.val:.4f} ({loss.avg:.4f})\t'
'Prec@1 {top1.val:.3f} ({top1.avg:.3f})\t'
'Prec@5 {top5.val:.3f} ({top5.avg:.3f})'.format(
epoch, i, len(train_loader), batch_time=batch_time,
data_time=data_time, loss=losses, top1=top1, top5=top5))
logger.debug('Epoch: [{0}][{1}/{2}]\t'
'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
'Data {data_time.val:.3f} ({data_time.avg:.3f})\t'
'Loss {loss.val:.4f} ({loss.avg:.4f})\t'
'Prec@1 {top1.val:.3f} ({top1.avg:.3f})\t'
'Prec@5 {top5.val:.3f} ({top5.avg:.3f})'.format(
epoch, i, len(train_loader), batch_time=batch_time,
data_time=data_time, loss=losses, top1=top1, top5=top5))


def validate(val_loader, model, criterion):
Expand Down Expand Up @@ -222,16 +231,16 @@ def validate(val_loader, model, criterion):
end = time.time()

if i % args.print_freq == 0:
print('Test: [{0}/{1}]\t'
'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
'Loss {loss.val:.4f} ({loss.avg:.4f})\t'
'Prec@1 {top1.val:.3f} ({top1.avg:.3f})\t'
'Prec@5 {top5.val:.3f} ({top5.avg:.3f})'.format(
i, len(val_loader), batch_time=batch_time, loss=losses,
top1=top1, top5=top5))

print(' * Prec@1 {top1.avg:.3f} Prec@5 {top5.avg:.3f}'
.format(top1=top1, top5=top5))
logger.debug('Test: [{0}/{1}]\t'
'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
'Loss {loss.val:.4f} ({loss.avg:.4f})\t'
'Prec@1 {top1.val:.3f} ({top1.avg:.3f})\t'
'Prec@5 {top5.val:.3f} ({top5.avg:.3f})'.format(
i, len(val_loader), batch_time=batch_time, loss=losses,
top1=top1, top5=top5))

logger.debug(' * Prec@1 {top1.avg:.3f} Prec@5 {top5.avg:.3f}'
.format(top1=top1, top5=top5))

return top1.avg

Expand Down