github/DeepFaceLab
1
0
Fork 0
mirror of https://github.com/iperov/DeepFaceLab.git synced 2025-07-12 16:13:51 -07:00
Code Issues Projects Releases Packages Wiki Activity Actions

removing trailing spaces

Browse source
This commit is contained in:
iperov 2019-03-19 23:53:27 +04:00
parent fa4e579b95
commit a3df04999c
61 changed files with 2110 additions and 2103 deletions
Download patch file Download diff file Expand all files Collapse all files

46
models/Model_FANSegmentator/Model.py
View file

@ -10,13 +10,13 @@ from interact import interact as io
class Model(ModelBase):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs,
ask_write_preview_history=False,
super().__init__(*args, **kwargs,
ask_write_preview_history=False,
ask_target_iter=False,
ask_sort_by_yaw=False,
ask_random_flip=False,
ask_src_scale_mod=False)
#override
def onInitialize(self):
exec(nnlib.import_all(), locals(), globals())
@ -24,33 +24,33 @@ class Model(ModelBase):
self.resolution = 256
self.face_type = FaceType.FULL
self.fan_seg = FANSegmentator(self.resolution,
FaceType.toString(self.face_type),
self.fan_seg = FANSegmentator(self.resolution,
FaceType.toString(self.face_type),
load_weights=not self.is_first_run(),
weights_file_root=self.get_model_root_path() )
if self.is_training_mode:
f = SampleProcessor.TypeFlags
f_type = f.FACE_ALIGN_FULL
self.set_training_data_generators ([
SampleGeneratorFace(self.training_data_src_path, debug=self.is_debug(), batch_size=self.batch_size,
sample_process_options=SampleProcessor.Options(random_flip=True, normalize_tanh = True ),
self.set_training_data_generators ([
SampleGeneratorFace(self.training_data_src_path, debug=self.is_debug(), batch_size=self.batch_size,
sample_process_options=SampleProcessor.Options(random_flip=True, normalize_tanh = True ),
output_sample_types=[ [f.TRANSFORMED | f_type | f.MODE_BGR_SHUFFLE, self.resolution],
[f.TRANSFORMED | f_type | f.MODE_M | f.FACE_MASK_FULL, self.resolution]
]),
SampleGeneratorFace(self.training_data_dst_path, debug=self.is_debug(), batch_size=self.batch_size,
sample_process_options=SampleProcessor.Options(random_flip=True, normalize_tanh = True ),
SampleGeneratorFace(self.training_data_dst_path, debug=self.is_debug(), batch_size=self.batch_size,
sample_process_options=SampleProcessor.Options(random_flip=True, normalize_tanh = True ),
output_sample_types=[ [f.TRANSFORMED | f_type | f.MODE_BGR_SHUFFLE, self.resolution]
])
])
#override
def onSave(self):
def onSave(self):
self.fan_seg.save_weights()
#override
def onTrainOneIter(self, generators_samples, generators_list):
target_src, target_src_mask = generators_samples[0]
@ -58,20 +58,20 @@ class Model(ModelBase):
loss = self.fan_seg.train_on_batch( [target_src], [target_src_mask] )
return ( ('loss', loss), )
#override
def onGetPreview(self, sample):
test_A = sample[0][0][0:4] #first 4 samples
test_B = sample[1][0][0:4] #first 4 samples
mAA = self.fan_seg.extract_from_bgr([test_A])
mBB = self.fan_seg.extract_from_bgr([test_B])
test_A, test_B, = [ np.clip( (x + 1.0)/2.0, 0.0, 1.0) for x in [test_A, test_B] ]
mAA = np.repeat ( mAA, (3,), -1)
mBB = np.repeat ( mBB, (3,), -1)
st = []
for i in range(0, len(test_A)):
st.append ( np.concatenate ( (
@ -79,7 +79,7 @@ class Model(ModelBase):
mAA[i],
test_A[i,:,:,0:3]*mAA[i],
), axis=1) )
st2 = []
for i in range(0, len(test_B)):
st2.append ( np.concatenate ( (
@ -87,7 +87,7 @@ class Model(ModelBase):
mBB[i],
test_B[i,:,:,0:3]*mBB[i],
), axis=1) )
return [ ('FANSegmentator', np.concatenate ( st, axis=0 ) ),
('never seen', np.concatenate ( st2, axis=0 ) ),
]