diff --git a/distiller/apputils/image_classifier.py b/distiller/apputils/image_classifier.py
index 8db49acb366b83de9c5323c82f38164b44ff810a..a6a859158b33a454fe7932f22e21a017675634ea 100755
--- a/distiller/apputils/image_classifier.py
+++ b/distiller/apputils/image_classifier.py
@@ -115,7 +115,6 @@ class ClassifierCompressor(object):
def validate_one_epoch(self, epoch, verbose=True):
"""Evaluate on validation set"""
self.load_datasets()
-
with collectors_context(self.activations_collectors["valid"]) as collectors:
top1, top5, vloss = validate(self.val_loader, self.model, self.criterion,
[self.pylogger], self.args, epoch)
@@ -128,9 +127,8 @@ class ClassifierCompressor(object):
OrderedDict([('Loss', vloss),
('Top1', top1),
('Top5', top5)]))
- distiller.log_training_progress(stats, None, epoch, steps_completed=0, total_steps=1, log_freq=1,
- loggers=[self.tflogger])
-
+ distiller.log_training_progress(stats, None, epoch, steps_completed=0,
+ total_steps=1, log_freq=1, loggers=[self.tflogger])
return top1, top5, vloss
def _finalize_epoch(self, epoch, perf_scores_history, top1, top5):
@@ -284,7 +282,6 @@ def init_classifier_compression_arg_parser():
help='Load a model without DataParallel wrapping it')
parser.add_argument('--thinnify', dest='thinnify', action='store_true', default=False,
help='physically remove zero-filters and create a smaller model')
-
distiller.quantization.add_post_train_quant_args(parser)
return parser
@@ -490,6 +487,34 @@ def train(train_loader, model, criterion, optimizer, epoch,
optimizer.step()
compression_scheduler.on_minibatch_end(epoch)
"""
+ def _log_training_progress():
+ # Log some statistics
+ errs = OrderedDict()
+ if not early_exit_mode(args):
+ errs['Top1'] = classerr.value(1)
+ errs['Top5'] = classerr.value(5)
+ else:
+ # for Early Exit case, the Top1 and Top5 stats are computed for each exit.
+ for exitnum in range(args.num_exits):
+ errs['Top1_exit' + str(exitnum)] = args.exiterrors[exitnum].value(1)
+ errs['Top5_exit' + str(exitnum)] = args.exiterrors[exitnum].value(5)
+
+ stats_dict = OrderedDict()
+ for loss_name, meter in losses.items():
+ stats_dict[loss_name] = meter.mean
+ stats_dict.update(errs)
+ stats_dict['LR'] = optimizer.param_groups[0]['lr']
+ stats_dict['Time'] = batch_time.mean
+ stats = ('Performance/Training/', stats_dict)
+
+ params = model.named_parameters() if args.log_params_histograms else None
+ distiller.log_training_progress(stats,
+ params,
+ epoch, steps_completed,
+ steps_per_epoch, args.print_freq,
+ loggers)
+
+
OVERALL_LOSS_KEY = 'Overall Loss'
OBJECTIVE_LOSS_KEY = 'Objective Loss'
@@ -570,31 +595,8 @@ def train(train_loader, model, criterion, optimizer, epoch,
steps_completed = (train_step+1)
if steps_completed % args.print_freq == 0:
- # Log some statistics
- errs = OrderedDict()
- if not early_exit_mode(args):
- errs['Top1'] = classerr.value(1)
- errs['Top5'] = classerr.value(5)
- else:
- # for Early Exit case, the Top1 and Top5 stats are computed for each exit.
- for exitnum in range(args.num_exits):
- errs['Top1_exit' + str(exitnum)] = args.exiterrors[exitnum].value(1)
- errs['Top5_exit' + str(exitnum)] = args.exiterrors[exitnum].value(5)
+ _log_training_progress()
- stats_dict = OrderedDict()
- for loss_name, meter in losses.items():
- stats_dict[loss_name] = meter.mean
- stats_dict.update(errs)
- stats_dict['LR'] = optimizer.param_groups[0]['lr']
- stats_dict['Time'] = batch_time.mean
- stats = ('Performance/Training/', stats_dict)
-
- params = model.named_parameters() if args.log_params_histograms else None
- distiller.log_training_progress(stats,
- params,
- epoch, steps_completed,
- steps_per_epoch, args.print_freq,
- loggers)
end = time.time()
#return acc_stats
# NOTE: this breaks previous behavior, which returned a history of (top1, top5) values
@@ -628,6 +630,29 @@ def _is_earlyexit(args):
def _validate(data_loader, model, criterion, loggers, args, epoch=-1):
+ def _log_validation_progress():
+ if not _is_earlyexit(args):
+ stats_dict = OrderedDict([('Loss', losses['objective_loss'].mean),
+ ('Top1', classerr.value(1)),
+ ('Top5', classerr.value(5))])
+ else:
+ stats_dict = OrderedDict()
+ stats_dict['Test'] = validation_step
+ for exitnum in range(args.num_exits):
+ la_string = 'LossAvg' + str(exitnum)
+ stats_dict[la_string] = args.losses_exits[exitnum].mean
+ # Because of the nature of ClassErrorMeter, if an exit is never taken during the batch,
+ # then accessing the value(k) will cause a divide by zero. So we'll build the OrderedDict
+ # accordingly and we will not print for an exit error when that exit is never taken.
+ if args.exit_taken[exitnum]:
+ t1 = 'Top1_exit' + str(exitnum)
+ t5 = 'Top5_exit' + str(exitnum)
+ stats_dict[t1] = args.exiterrors[exitnum].value(1)
+ stats_dict[t5] = args.exiterrors[exitnum].value(5)
+ stats = ('Performance/Validation/', stats_dict)
+ distiller.log_training_progress(stats, None, epoch, steps_completed,
+ total_steps, args.print_freq, loggers)
+
"""Execute the validation/test loop."""
losses = {'objective_loss': tnt.AverageValueMeter()}
classerr = tnt.ClassErrorMeter(accuracy=True, topk=(1, 5))
@@ -676,29 +701,8 @@ def _validate(data_loader, model, criterion, loggers, args, epoch=-1):
steps_completed = (validation_step+1)
if steps_completed % args.print_freq == 0:
- if not _is_earlyexit(args):
- stats = ('',
- OrderedDict([('Loss', losses['objective_loss'].mean),
- ('Top1', classerr.value(1)),
- ('Top5', classerr.value(5))]))
- else:
- stats_dict = OrderedDict()
- stats_dict['Test'] = validation_step
- for exitnum in range(args.num_exits):
- la_string = 'LossAvg' + str(exitnum)
- stats_dict[la_string] = args.losses_exits[exitnum].mean
- # Because of the nature of ClassErrorMeter, if an exit is never taken during the batch,
- # then accessing the value(k) will cause a divide by zero. So we'll build the OrderedDict
- # accordingly and we will not print for an exit error when that exit is never taken.
- if args.exit_taken[exitnum]:
- t1 = 'Top1_exit' + str(exitnum)
- t5 = 'Top5_exit' + str(exitnum)
- stats_dict[t1] = args.exiterrors[exitnum].value(1)
- stats_dict[t5] = args.exiterrors[exitnum].value(5)
- stats = ('Performance/Validation/', stats_dict)
-
- distiller.log_training_progress(stats, None, epoch, steps_completed,
- total_steps, args.print_freq, loggers)
+ _log_validation_progress()
+
if not _is_earlyexit(args):
msglogger.info('==> Top1: %.3f Top5: %.3f Loss: %.3f\n',
classerr.value()[0], classerr.value()[1], losses['objective_loss'].mean)