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iperov 2019-04-20 15:22:00 +04:00
parent 046649e6be
commit 3ffdc50576
2 changed files with 28 additions and 22 deletions
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12
models/ModelBase.py
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@ -85,7 +85,8 @@ class ModelBase(object):
else:
self.options['write_preview_history'] = self.options.get('write_preview_history', False)
if ask_target_iter and (self.iter == 0 or ask_override):
if ask_target_iter:
if (self.iter == 0 or ask_override):
self.options['target_iter'] = max(0, io.input_int("Target iteration (skip:unlimited/default) : ", 0))
else:
self.options['target_iter'] = max(model_data.get('target_iter',0), self.options.get('target_epoch',0))
@ -98,17 +99,20 @@ class ModelBase(object):
else:
self.options['batch_size'] = self.options.get('batch_size', 0)
if ask_sort_by_yaw and (self.iter == 0):
if ask_sort_by_yaw:
if (self.iter == 0):
self.options['sort_by_yaw'] = io.input_bool("Feed faces to network sorted by yaw? (y/n ?:help skip:n) : ", False, help_message="NN will not learn src face directions that don't match dst face directions." )
else:
self.options['sort_by_yaw'] = self.options.get('sort_by_yaw', False)
if ask_random_flip and (self.iter == 0):
if ask_random_flip:
if (self.iter == 0):
self.options['random_flip'] = io.input_bool("Flip faces randomly? (y/n ?:help skip:y) : ", True, help_message="Predicted face will look more naturally without this option, but src faceset should cover all face directions as dst faceset.")
else:
self.options['random_flip'] = self.options.get('random_flip', True)
if ask_src_scale_mod and (self.iter == 0):
if ask_src_scale_mod:
if (self.iter == 0):
self.options['src_scale_mod'] = np.clip( io.input_int("Src face scale modifier % ( -30...30, ?:help skip:0) : ", 0, help_message="If src face shape is wider than dst, try to decrease this value to get a better result."), -30, 30)
else:
self.options['src_scale_mod'] = self.options.get('src_scale_mod', 0)

10
samplelib/SampleGeneratorImageTemporal.py
View file

@ -42,11 +42,13 @@ class SampleGeneratorImageTemporal(SampleGeneratorBase):
if samples_len == 0:
raise ValueError('No training data provided.')
if samples_len - self.temporal_image_count < 0:
mult_max = 4
l = samples_len - (self.temporal_image_count-1)*mult_max + 1
if l < 0:
raise ValueError('Not enough samples to fit temporal line.')
shuffle_idxs = []
samples_sub_len = samples_len - self.temporal_image_count + 1
samples_sub_len = samples_len - l + 1
while True:
@ -60,9 +62,9 @@ class SampleGeneratorImageTemporal(SampleGeneratorBase):
idx = shuffle_idxs.pop()
temporal_samples = []
mult = np.random.randint(mult_max)
for i in range( self.temporal_image_count ):
sample = samples[ idx+i ]
sample = samples[ idx+i*mult ]
try:
temporal_samples += SampleProcessor.process (sample, self.sample_process_options, self.output_sample_types, self.debug)
except: