mirror of
https://github.com/iperov/DeepFaceLab.git
synced 2025-03-12 20:42:45 -07:00
50f892d57d
If you want, you can manually remove unnecessary angles from src faceset after sort by yaw. Optimized sample generators (CPU workers). Now they consume less amount of RAM and work faster. added 4.2.other) data_src/dst util faceset pack.bat Packs /aligned/ samples into one /aligned/samples.pak file. After that, all faces will be deleted. 4.2.other) data_src/dst util faceset unpack.bat unpacks faces from /aligned/samples.pak to /aligned/ dir. After that, samples.pak will be deleted. Packed faceset load and work faster.
104 lines
4.4 KiB
Python
104 lines
4.4 KiB
Python
import numpy as np
|
|
|
|
from nnlib import nnlib, TernausNet
|
|
from models import ModelBase
|
|
from facelib import FaceType
|
|
from samplelib import *
|
|
from interact import interact as io
|
|
|
|
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_random_flip=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, 11:48} )
|
|
|
|
self.resolution = 256
|
|
self.face_type = FaceType.FULL if self.options['face_type'] == 'f' else FaceType.HALF
|
|
|
|
model_name = 'FANSeg'
|
|
self.fan_seg = TernausNet(model_name, 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_RANDOM_HSV_SHIFT), 'resolution' : self.resolution, 'motion_blur':(25, 5), 'gaussian_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_RANDOM_HSV_SHIFT), '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_Am = sample[0][1][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)
|
|
|
|
test_Am = np.repeat ( test_Am, (3,), -1)
|
|
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],
|
|
test_Am[i],
|
|
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 ) ),
|
|
]
|