From 55c2d1dc581ba1940d7a602005f6a15c0971af19 Mon Sep 17 00:00:00 2001
From: barrh <elhararb@gmail.com>
Date: Mon, 9 Sep 2019 00:30:10 +0300
Subject: [PATCH] Fix epochs limitation check
This patch moves the check on remaining training epochs into the training loop, as it is irrelevant for non-training invocation of ClassifierCompressor.
Thus, allowing resumed checkpoints be evaluated regardless of the epochs limitation.
---
distiller/apputils/image_classifier.py | 14 +++++++-------
1 file changed, 7 insertions(+), 7 deletions(-)
diff --git a/distiller/apputils/image_classifier.py b/distiller/apputils/image_classifier.py
index f2f2a57..c5cd3f6 100755
--- a/distiller/apputils/image_classifier.py
+++ b/distiller/apputils/image_classifier.py
@@ -152,6 +152,12 @@ class ClassifierCompressor(object):
validate_one_epoch
finalize_epoch
"""
+ if self.start_epoch >= self.ending_epoch:
+ msglogger.error(
+ 'epoch count is too low, starting epoch is {} but total epochs set to {}'.format(
+ self.start_epoch, self.ending_epoch))
+ raise ValueError('Epochs parameter is too low. Nothing to do.')
+
# Load the datasets lazily
self.load_datasets()
@@ -396,13 +402,7 @@ def _init_learner(args):
elif compression_scheduler is None:
compression_scheduler = distiller.CompressionScheduler(model)
- ending_epoch = args.epochs
- if start_epoch >= ending_epoch:
- msglogger.error(
- 'epoch count is too low, starting epoch is {} but total epochs set to {}'.format(
- start_epoch, ending_epoch))
- raise ValueError('Epochs parameter is too low. Nothing to do.')
- return model, compression_scheduler, optimizer, start_epoch, ending_epoch
+ return model, compression_scheduler, optimizer, start_epoch, args.epochs
def create_activation_stats_collectors(model, *phases):
--
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