class Model(ModelBase):
def __init__(self, *args, **kwargs):
super().__init__(*args,
**kwargs,
ask_enable_autobackup=False,
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 onInitializeOptions(self, is_first_run, ask_override):
default_face_type = 'f'
if is_first_run:
self.options['face_type'] = io.input_str(
"Half or Full face? (h/f, ?:help skip:f) : ",
default_face_type, ['h', 'f'],
help_message="").lower()
else:
self.options['face_type'] = self.options.get(
'face_type', default_face_type)
#override
def onInitialize(self):
exec(nnlib.import_all(), locals(), globals())
self.set_vram_batch_requirements({1.5: 4})
self.resolution = 256
self.face_type = FaceType.FULL if self.options[
'face_type'] == 'f' else FaceType.HALF
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(),
training=True)
if self.is_training_mode:
t = SampleProcessor.Types
face_type = t.FACE_TYPE_FULL if self.options[
'face_type'] == 'f' else t.FACE_TYPE_HALF
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),
output_sample_types=[
{
'types': (t.IMG_WARPED_TRANSFORMED, face_type,
t.MODE_BGR_SHUFFLE),
'resolution':
self.resolution,
'motion_blur': (25, 5),
'border_replicate':
False
},
{
'types':
(t.IMG_WARPED_TRANSFORMED, face_type, t.MODE_M),
'resolution':
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),
output_sample_types=[
{
'types':
(t.IMG_TRANSFORMED, face_type, t.MODE_BGR_SHUFFLE),
'resolution':
self.resolution
},
])
])
#override
def onSave(self):
self.fan_seg.save_weights()
#override
def onTrainOneIter(self, generators_samples, generators_list):
target_src, target_src_mask = generators_samples[0]
loss = self.fan_seg.train(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(test_A)
mBB = self.fan_seg.extract(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((
test_A[i, :, :, 0:3],
mAA[i],
test_A[i, :, :, 0:3] * mAA[i],
),
axis=1))
st2 = []
for i in range(0, len(test_B)):
st2.append(
np.concatenate((
test_B[i, :, :, 0:3],
mBB[i],
test_B[i, :, :, 0:3] * mBB[i],
),
axis=1))
return [
('training data', np.concatenate(st, axis=0)),
('evaluating data', np.concatenate(st2, axis=0)),
]
class Model(ModelBase):
def __init__(self, *args, **kwargs):
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())
self.set_vram_batch_requirements({1.5: 4})
self.resolution = 256
self.face_type = FaceType.FULL
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(),
training=True)
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),
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),
output_sample_types=[[
f.TRANSFORMED | f_type | f.MODE_BGR_SHUFFLE,
self.resolution
]])
])
#override
def onSave(self):
self.fan_seg.save_weights()
#override
def onTrainOneIter(self, generators_samples, generators_list):
target_src, target_src_mask = generators_samples[0]
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((
test_A[i, :, :, 0:3],
mAA[i],
test_A[i, :, :, 0:3] * mAA[i],
),
axis=1))
st2 = []
for i in range(0, len(test_B)):
st2.append(
np.concatenate((
test_B[i, :, :, 0:3],
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)),
]
class Model(ModelBase):
def __init__(self, *args, **kwargs):
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 onInitializeOptions(self, is_first_run, ask_override):
default_face_type = 'f'
if is_first_run:
self.options['face_type'] = io.input_str(
"Half or Full face? (h/f, ?:help skip:f) : ",
default_face_type, ['h', 'f'],
help_message=
"Half face has better resolution, but covers less area of cheeks."
).lower()
else:
self.options['face_type'] = self.options.get(
'face_type', default_face_type)
#override
def onInitialize(self):
exec(nnlib.import_all(), locals(), globals())
self.set_vram_batch_requirements({1.5: 4})
self.resolution = 256
self.face_type = FaceType.FULL if self.options[
'face_type'] == 'f' else FaceType.HALF
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(),
training=True)
if self.is_training_mode:
f = SampleProcessor.TypeFlags
face_type = f.FACE_TYPE_FULL if self.options[
'face_type'] == 'f' else f.FACE_TYPE_HALF
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, motion_blur=[25, 1]),
output_sample_types=[[
f.WARPED_TRANSFORMED | face_type | f.MODE_BGR_SHUFFLE
| f.OPT_APPLY_MOTION_BLUR, self.resolution
],
[
f.WARPED_TRANSFORMED | face_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),
output_sample_types=[[
f.TRANSFORMED | face_type | f.MODE_BGR_SHUFFLE,
self.resolution
]])
])
#override
def onSave(self):
self.fan_seg.save_weights()
#override
def onTrainOneIter(self, generators_samples, generators_list):
target_src, target_src_mask = generators_samples[0]
loss, acc = self.fan_seg.train_on_batch([target_src],
[target_src_mask])
return (('loss', loss), ('acc', acc))
#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(test_A)
mBB = self.fan_seg.extract(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((
test_A[i, :, :, 0:3],
mAA[i],
test_A[i, :, :, 0:3] * mAA[i],
),
axis=1))
st2 = []
for i in range(0, len(test_B)):
st2.append(
np.concatenate((
test_B[i, :, :, 0:3],
mBB[i],
test_B[i, :, :, 0:3] * mBB[i],
),
axis=1))
return [
('training data', np.concatenate(st, axis=0)),
('evaluating data', np.concatenate(st2, axis=0)),
]
class Model(ModelBase):
#override
def onInitialize(self):
exec(nnlib.import_all(), locals(), globals())
self.set_vram_batch_requirements({1.5: 4})
self.resolution = 256
self.face_type = FaceType.FULL
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 #if self.face_type == FaceType.FULL else f.FACE_ALIGN_HALF
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=self.random_flip,
normalize_tanh=True,
scale_range=np.array([-0.05, 0.05]) +
self.src_scale_mod / 100.0),
output_sample_types=[
[f.TRANSFORMED | f_type | f.MODE_BGR, 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=self.random_flip,
normalize_tanh=True,
scale_range=np.array([-0.05, 0.05]) +
self.src_scale_mod / 100.0),
output_sample_types=[[
f.TRANSFORMED | f_type | f.MODE_BGR, self.resolution
]])
])
#override
def onSave(self):
self.fan_seg.save_weights()
#override
def onTrainOneIter(self, generators_samples, generators_list):
target_src, target_src_mask = generators_samples[0]
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])
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((
test_A[i, :, :, 0:3],
mAA[i],
test_A[i, :, :, 0:3] * mAA[i],
),
axis=1))
st2 = []
for i in range(0, len(test_B)):
st2.append(
np.concatenate((
test_B[i, :, :, 0:3],
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)),
]
def predictor_func(self, face):
face_64_bgr = face[..., 0:3]
face_64_mask = np.expand_dims(face[..., 3], -1)
x, mx = self.src_view([np.expand_dims(face_64_bgr, 0)])
x, mx = x[0], mx[0]
return np.concatenate((x, mx), -1)
#override
def get_converter(self):
from converters import ConverterMasked
return ConverterMasked(self.predictor_func,
predictor_input_size=64,
output_size=64,
face_type=FaceType.HALF,
base_erode_mask_modifier=100,
base_blur_mask_modifier=100)
