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FANSeg : reverting to use it the same as in DFL 1.0

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Colombo 2020-03-08 22:25:11 +04:00
parent c371818eef
commit 121c0cfc0f
1 changed files with 15 additions and 14 deletions
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29
models/Model_FANSeg/Model.py
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@ -19,7 +19,7 @@ class FANSegModel(ModelBase):
yn_str = {True:'y',False:'n'}
#default_resolution = 256
default_face_type = self.options['face_type'] = self.load_or_def_option('face_type', 'wf')
#default_face_type = self.options['face_type'] = self.load_or_def_option('face_type', 'f')
ask_override = self.ask_override()
if self.is_first_run() or ask_override:
@ -27,12 +27,12 @@ class FANSegModel(ModelBase):
self.ask_target_iter()
self.ask_batch_size(4)
if self.is_first_run():
#resolution = io.input_int("Resolution", default_resolution, add_info="64-512")
#resolution = np.clip ( (resolution // 16) * 16, 64, 512)
#self.options['resolution'] = resolution
self.options['face_type'] = io.input_str ("Face type", default_face_type, ['h','mf','f','wf']).lower()
#if self.is_first_run():
#resolution = io.input_int("Resolution", default_resolution, add_info="64-512")
#resolution = np.clip ( (resolution // 16) * 16, 64, 512)
#self.options['resolution'] = resolution
#self.options['face_type'] = io.input_str ("Face type", default_face_type, ['f']).lower()
#override
def on_initialize(self):
device_config = nn.getCurrentDeviceConfig()
@ -43,11 +43,12 @@ class FANSegModel(ModelBase):
devices = device_config.devices
self.resolution = resolution = 256#self.options['resolution']
self.face_type = {'h' : FaceType.HALF,
'mf' : FaceType.MID_FULL,
'f' : FaceType.FULL,
'wf' : FaceType.WHOLE_FACE}[ self.options['face_type'] ]
#self.face_type = {'h' : FaceType.HALF,
# 'mf' : FaceType.MID_FULL,
# 'f' : FaceType.FULL,
# 'wf' : FaceType.WHOLE_FACE}[ self.options['face_type'] ]
self.face_type = FaceType.FULL
place_model_on_cpu = len(devices) == 0
models_opt_device = '/CPU:0' if place_model_on_cpu else '/GPU:0'
@ -125,8 +126,8 @@ class FANSegModel(ModelBase):
src_generator = SampleGeneratorFace(training_data_src_path, random_ct_samples_path=training_data_src_path, debug=self.is_debug(), batch_size=self.get_batch_size(),
sample_process_options=SampleProcessor.Options(random_flip=True),
output_sample_types = [ {'sample_type': SampleProcessor.SampleType.FACE_IMAGE, 'ct_mode':'idt', 'warp':True, 'transform':True, 'channel_type' : SampleProcessor.ChannelType.BGR, 'face_type':self.face_type, 'motion_blur':(25, 5), 'gaussian_blur':(25,5), 'data_format':nn.data_format, 'resolution': resolution},
{'sample_type': SampleProcessor.SampleType.FACE_MASK, 'warp':True, 'transform':True, 'channel_type' : SampleProcessor.ChannelType.G, 'face_mask_type' : SampleProcessor.FaceMaskType.NONE, 'face_type':self.face_type, 'data_format':nn.data_format, 'resolution': resolution},
output_sample_types = [ {'sample_type': SampleProcessor.SampleType.FACE_IMAGE, 'ct_mode':'idt', 'warp':True, 'transform':True, 'channel_type' : SampleProcessor.ChannelType.BGR, 'face_type':self.face_type, 'motion_blur':(25, 5), 'gaussian_blur':(25,5), 'data_format':nn.data_format, 'resolution': resolution},
{'sample_type': SampleProcessor.SampleType.FACE_MASK, 'warp':True, 'transform':True, 'channel_type' : SampleProcessor.ChannelType.G, 'face_mask_type' : SampleProcessor.FaceMaskType.FULL_FACE, 'face_type':self.face_type, 'data_format':nn.data_format, 'resolution': resolution},
],
generators_count=src_generators_count )