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fixed forced bs

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This commit is contained in:
Brigham Lysenko 2019-08-24 18:49:09 -06:00
commit 1d3f985ced
1 changed files with 7 additions and 5 deletions
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12
models/ModelBase.py
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@ -140,21 +140,23 @@ class ModelBase(object):
if ask_batch_size and (self.iter == 0 or ask_override):
default_batch_size = 0 if self.iter == 0 else self.options.get('batch_size',0)
self.options['batch_cap'] = max(0, io.input_int("Batch_size (?:help skip:%d) : " % self.options.get('batch_cap', 16),self.options.get('batch_cap', 16),
self.options['batch_cap'] = max(0, io.input_int("Batch_size (?:help skip:%d) : " % self.options.get('batch_cap', 1),self.options.get('batch_cap', 1),
help_message="Larger batch size is better for NN's"
" generalization, but it can cause Out of"
" Memory error. Tune this value for your"
" videocard manually."))
self.options['ping_pong'] = io.input_bool(
"Enable ping-pong? (y/n ?:help skip:%s) : " % self.options.get('ping_pong', False),
"Enable ping-pong? (y/n ?:help skip:%s) : " % yn_str[self.options.get('ping_pong', False)],
self.options.get('ping_pong', False),
help_message="Cycles batch size between 1 and chosen batch size, simulating super convergence")
self.options['paddle'] = self.options.get('paddle','ping')
if self.options.get('ping_pong',False):
self.options['ping_pong_iter'] = max(0, io.input_int("Ping-pong iteration (skip:1000/default) : ", 1000))
else:
self.options['batch_size'] = self.options.get('batch_cap', 1)
else:
self.options['batch_cap'] = self.options.get('batch_cap', 16)
self.options['batch_cap'] = self.options.get('batch_cap', 1)
self.options['ping_pong'] = self.options.get('ping_pong', False)
self.options['ping_pong_iter'] = self.options.get('ping_pong_iter',1000)
@ -197,13 +199,13 @@ class ModelBase(object):
self.options.pop('target_iter')
self.batch_size = self.options.get('batch_size', 8)
self.batch_cap = self.options.get('batch_cap',16)
self.batch_cap = self.options.get('batch_cap',1)
self.ping_pong_iter = self.options.get('ping_pong_iter',1000)
self.sort_by_yaw = self.options.get('sort_by_yaw',False)
self.random_flip = self.options.get('random_flip',True)
if self.batch_cap == 0:
self.options['batch_cap'] = self.batch_size
self.batch_cap = self.options.get('batch_cap',16)
self.batch_cap = self.options.get('batch_cap',1)
self.src_scale_mod = self.options.get('src_scale_mod',0)
if self.src_scale_mod == 0 and 'src_scale_mod' in self.options: