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TalosOfCrete 2020-06-08 17:42:24 -05:00
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models/Model_SAEHD/Model.py
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@ -66,7 +66,7 @@ class SAEHDModel(ModelBase):
self.options['resolution'] = resolution
self.options['face_type'] = io.input_str ("Face type", default_face_type, ['h','mf','f','wf','head'], help_message="Half / mid face / full face / whole face / head. Half face has better resolution, but covers less area of cheeks. Mid face is 30% wider than half face. 'Whole face' covers full area of face including forehead. 'head' covers full head, but requires XSeg for src and dst faceset.").lower()
self.options['archi'] = io.input_str ("AE architecture", default_archi, ['df','liae','dfhd','liaehd','dfuhd','liaeuhd'], help_message="'df' keeps faces more natural.\n'liae' can fix overly different face shapes.\n'hd' are experimental versions.").lower()
self.options['separable_base'] = io.input_bool ("Use depthwise separable convolutions", default_separable_base, help_message="Use lighter and more effecient layers in any parts (encoder, bottleneck, and decoder) of the model you want, which speeds up iterations (~15-200%) and reduces memory usage considerably (~60%). However, more iterations may be required to get a good model and there may be some degree of quality loss (can be compensated for with higher enc/dec dims).")
self.options['separable_base'] = io.input_bool ("Use depthwise separable convolutions", default_separable_base, help_message="Use lighter layers in any parts (encoder, bottleneck, and decoder) of the model you want, which speeds up iterations (~15-200%) and reduces memory usage (~60%). WARNING: more iterations may be required, and there may be some degree of quality loss")
if self.options['separable_base']:
self.options['separable_enc'] = io.input_bool ("Use depthwise separable convolutions in the encoder", default_separable_enc, help_message="This is the part of the model second most impacted by using these more efficient layers in terms of better iteration speed, memory savings, possible quality loss, and possible increase in required iterations as it may have anywhere from one to four downscaling operations for UHD and HD architectures respectively.")