refactorings
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@@ -325,12 +325,12 @@ class SAEModel(ModelBase):
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self.src_sample_losses = []
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self.dst_sample_losses = []
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f = SampleProcessor.TypeFlags
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face_type = f.FACE_TYPE_FULL if self.options['face_type'] == 'f' else f.FACE_TYPE_HALF
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t = SampleProcessor.Types
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face_type = t.FACE_TYPE_FULL if self.options['face_type'] == 'f' else t.FACE_TYPE_HALF
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output_sample_types=[ [f.WARPED_TRANSFORMED | face_type | f.MODE_BGR, resolution] ]
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output_sample_types += [ [f.TRANSFORMED | face_type | f.MODE_BGR, resolution // (2**i) ] for i in range(ms_count)]
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output_sample_types += [ [f.TRANSFORMED | face_type | f.MODE_M | f.FACE_MASK_FULL, resolution // (2**i) ] for i in range(ms_count)]
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output_sample_types = [ {'types' : (t.IMG_WARPED_TRANSFORMED, face_type, t.MODE_BGR), 'resolution':resolution} ]
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output_sample_types += [ {'types' : (t.IMG_TRANSFORMED, face_type, t.MODE_BGR), 'resolution': resolution // (2**i) } for i in range(ms_count)]
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output_sample_types += [ {'types' : (t.IMG_TRANSFORMED, face_type, t.MODE_M, t.FACE_MASK_FULL), 'resolution': resolution // (2**i) } for i in range(ms_count)]
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self.set_training_data_generators ([
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SampleGeneratorFace(self.training_data_src_path, sort_by_yaw_target_samples_path=self.training_data_dst_path if self.sort_by_yaw else None,
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