github/DeepFaceLab
1
0
Fork 0
mirror of https://github.com/iperov/DeepFaceLab.git synced 2025-07-06 13:02:15 -07:00
Code Issues Projects Releases Packages Wiki Activity Actions

all models: removed options 'src_scale_mod', and 'sort samples by yaw as target'

Browse source 
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.
This commit is contained in:
Colombo 2019-12-21 23:16:55 +04:00
parent 8866dce22e
commit 50f892d57d
26 changed files with 577 additions and 433 deletions
Download patch file Download diff file Expand all files Collapse all files

11
models/Model_SAEHD/Model.py
View file

@ -529,26 +529,23 @@ class SAEHDModel(ModelBase):
training_data_src_path = self.training_data_src_path
training_data_dst_path = self.training_data_dst_path
sort_by_yaw = self.sort_by_yaw
if self.pretrain and self.pretraining_data_path is not None:
training_data_src_path = self.pretraining_data_path
training_data_dst_path = self.pretraining_data_path
sort_by_yaw = False
t_img_warped = t.IMG_WARPED_TRANSFORMED if self.options['random_warp'] else t.IMG_TRANSFORMED
self.set_training_data_generators ([
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 self.options['ct_mode'] != 'none' else None,
debug=self.is_debug(), batch_size=self.batch_size, use_caching=False,
sample_process_options=SampleProcessor.Options(random_flip=self.random_flip, scale_range=np.array([-0.05, 0.05])+self.src_scale_mod / 100.0 ),
SampleGeneratorFace(training_data_src_path, random_ct_samples_path=training_data_dst_path if self.options['ct_mode'] != 'none' else None,
debug=self.is_debug(), batch_size=self.batch_size,
sample_process_options=SampleProcessor.Options(random_flip=self.random_flip, scale_range=np.array([-0.05, 0.05]) ),
output_sample_types = [ {'types' : (t_img_warped, face_type, t_mode_bgr), 'resolution':resolution, 'ct_mode': self.options['ct_mode'] },
{'types' : (t.IMG_TRANSFORMED, face_type, t_mode_bgr), 'resolution': resolution, 'ct_mode': self.options['ct_mode'] },
{'types' : (t.IMG_TRANSFORMED, face_type, t.MODE_M), 'resolution': resolution } ]
),
SampleGeneratorFace(training_data_dst_path, debug=self.is_debug(), batch_size=self.batch_size, use_caching=False,
SampleGeneratorFace(training_data_dst_path, debug=self.is_debug(), batch_size=self.batch_size,
sample_process_options=SampleProcessor.Options(random_flip=self.random_flip, ),
output_sample_types = [ {'types' : (t_img_warped, face_type, t_mode_bgr), 'resolution':resolution},
{'types' : (t.IMG_TRANSFORMED, face_type, t_mode_bgr), 'resolution': resolution},