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fixed bug fix

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This commit is contained in:
Brigham Lysenko 2019-08-15 14:01:16 -06:00
commit 4adeb0aa79
2 changed files with 43 additions and 12 deletions
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14
models/ModelBase.py
View file

@ -145,16 +145,16 @@ class ModelBase(object):
" Memory error. Tune this value for your" " Memory error. Tune this value for your"
" videocard manually.")) " videocard manually."))
self.options['ping_pong'] = io.input_bool( self.options['ping_pong'] = io.input_bool(
"Enable ping-pong? (y/n ?:help skip:%s) : " % (yn_str[True]), "Enable ping-pong? (y/n ?:help skip:%s) : " % self.options.get('batch_cap', False),
True, self.options.get('batch_cap', False),
help_message="Cycles batch size between 1 and chosen batch size, simulating super convergence") help_message="Cycles batch size between 1 and chosen batch size, simulating super convergence")
self.options['paddle'] = self.options.get('paddle','ping') self.options['paddle'] = self.options.get('paddle','ping')
if self.options.get('ping_pong',True): if self.options.get('ping_pong',False):
self.options['ping_pong_iter'] = max(0, io.input_int("Ping-pong iteration (skip:1000/default) : ", 1000)) self.options['ping_pong_iter'] = max(0, io.input_int("Ping-pong iteration (skip:1000/default) : ", 1000))
else: else:
self.options['batch_cap'] = self.options.get('batch_cap', 16) self.options['batch_cap'] = self.options.get('batch_cap', 16)
self.options['ping_pong'] = self.options.get('ping_pong', True) self.options['ping_pong'] = self.options.get('ping_pong', False)
self.options['ping_pong_iter'] = self.options.get('ping_pong_iter',1000) self.options['ping_pong_iter'] = self.options.get('ping_pong_iter',1000)
if ask_sort_by_yaw: if ask_sort_by_yaw:
@ -525,10 +525,10 @@ class ModelBase(object):
def train_one_iter(self): def train_one_iter(self):
if self.iter == 1 and self.options.get('ping_pong', True): if self.iter == 1 and self.options.get('ping_pong', False):
self.set_batch_size(1) self.set_batch_size(1)
self.paddle = 'ping' self.paddle = 'ping'
elif not self.options.get('ping_pong', True) and self.batch_cap != self.batch_size: elif not self.options.get('ping_pong', False) and self.batch_cap != self.batch_size:
self.set_batch_size(self.batch_cap) self.set_batch_size(self.batch_cap)
sample = self.generate_next_sample() sample = self.generate_next_sample()
iter_time = time.time() iter_time = time.time()
@ -556,7 +556,7 @@ class ModelBase(object):
img = (np.concatenate([preview_lh, preview], axis=0) * 255).astype(np.uint8) img = (np.concatenate([preview_lh, preview], axis=0) * 255).astype(np.uint8)
cv2_imwrite(filepath, img) cv2_imwrite(filepath, img)
if self.iter % self.ping_pong_iter == 0 and self.iter != 0 and self.options.get('ping_pong', True): if self.iter % self.ping_pong_iter == 0 and self.iter != 0 and self.options.get('ping_pong', False):
if self.batch_size == self.batch_cap: if self.batch_size == self.batch_cap:
self.paddle = 'pong' self.paddle = 'pong'
if self.batch_size > self.batch_cap: if self.batch_size > self.batch_cap:

41
models/Model_SAE/Model.py
View file

@ -179,6 +179,7 @@ class SAEModel(ModelBase):
global apply_random_ct
apply_random_ct = self.options.get('apply_random_ct', ColorTransferMode.NONE) apply_random_ct = self.options.get('apply_random_ct', ColorTransferMode.NONE)
masked_training = True masked_training = True
@ -457,11 +458,12 @@ class SAEModel(ModelBase):
global t_mode_bgr global t_mode_bgr
t_mode_bgr = t.MODE_BGR if not self.pretrain else t.MODE_BGR_SHUFFLE t_mode_bgr = t.MODE_BGR if not self.pretrain else t.MODE_BGR_SHUFFLE
global training_data_src_path
training_data_src_path = self.training_data_src_path training_data_src_path = self.training_data_src_path
global training_data_dst_path
training_data_dst_path= self.training_data_dst_path training_data_dst_path= self.training_data_dst_path
global sort_by_yaw
sort_by_yaw = self.sort_by_yaw sort_by_yaw = self.sort_by_yaw
if self.pretrain and self.pretraining_data_path is not None: if self.pretrain and self.pretraining_data_path is not None:
@ -537,9 +539,38 @@ class SAEModel(ModelBase):
# override # override
def set_batch_size(self, batch_size): def set_batch_size(self, batch_size):
self.batch_size = batch_size self.set_training_data_generators(None)
for i, generator in enumerate(self.generator_list): self.set_training_data_generators([
generator.update_batch(batch_size) SampleGeneratorFace(training_data_src_path,
sort_by_yaw_target_samples_path=training_data_dst_path if sort_by_yaw else None,
random_ct_samples_path=training_data_dst_path if apply_random_ct != ColorTransferMode.NONE else None,
debug=self.is_debug(), batch_size=batch_size,
sample_process_options=SampleProcessor.Options(random_flip=self.random_flip,
scale_range=np.array([-0.05,
0.05]) + self.src_scale_mod / 100.0),
output_sample_types=[{'types': (
t.IMG_WARPED_TRANSFORMED, face_type, t_mode_bgr),
'resolution': resolution, 'apply_ct': apply_random_ct}] + \
[{'types': (t.IMG_TRANSFORMED, face_type, t_mode_bgr),
'resolution': resolution // (2 ** i),
'apply_ct': apply_random_ct} for i in range(ms_count)] + \
[{'types': (t.IMG_TRANSFORMED, face_type, t.MODE_M),
'resolution': resolution // (2 ** i)} for i in
range(ms_count)]
),
SampleGeneratorFace(training_data_dst_path, debug=self.is_debug(), batch_size=batch_size,
sample_process_options=SampleProcessor.Options(random_flip=self.random_flip, ),
output_sample_types=[{'types': (
t.IMG_WARPED_TRANSFORMED, face_type, t_mode_bgr),
'resolution': resolution}] + \
[{'types': (t.IMG_TRANSFORMED, face_type, t_mode_bgr),
'resolution': resolution // (2 ** i)} for i in
range(ms_count)] + \
[{'types': (t.IMG_TRANSFORMED, face_type, t.MODE_M),
'resolution': resolution // (2 ** i)} for i in
range(ms_count)])
])
# override # override
def onTrainOneIter(self, generators_samples, generators_list): def onTrainOneIter(self, generators_samples, generators_list):