class Model(ModelBase): def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs, ask_write_preview_history=False, ask_target_iter=False, ask_sort_by_yaw=False, ask_random_flip=False, ask_src_scale_mod=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= "Half face has better resolution, but covers less area of cheeks." ).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}) self.resolution = 256 self.face_type = FaceType.FULL if self.options[ 'face_type'] == 'f' else FaceType.HALF self.fan_seg = FANSegmentator( 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: f = SampleProcessor.TypeFlags face_type = f.FACE_TYPE_FULL if self.options[ 'face_type'] == 'f' else f.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, motion_blur=[25, 1]), output_sample_types=[[ f.WARPED_TRANSFORMED | face_type | f.MODE_BGR_SHUFFLE | f.OPT_APPLY_MOTION_BLUR, self.resolution ], [ f.WARPED_TRANSFORMED | face_type | f.MODE_M | f.FACE_MASK_FULL, 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=[[ f.TRANSFORMED | face_type | f.MODE_BGR_SHUFFLE, 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, acc = self.fan_seg.train_on_batch([target_src], [target_src_mask]) return (('loss', loss), ('acc', acc)) #override def onGetPreview(self, sample): test_A = sample[0][0][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) 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], 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)), ]
class Model(ModelBase): #override def onInitialize(self): exec(nnlib.import_all(), locals(), globals()) self.set_vram_batch_requirements({1.5: 4}) self.resolution = 256 self.face_type = FaceType.FULL self.fan_seg = FANSegmentator( self.resolution, FaceType.toString(self.face_type), load_weights=not self.is_first_run(), weights_file_root=self.get_model_root_path()) if self.is_training_mode: f = SampleProcessor.TypeFlags f_type = f.FACE_ALIGN_FULL #if self.face_type == FaceType.FULL else f.FACE_ALIGN_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=self.random_flip, normalize_tanh=True, scale_range=np.array([-0.05, 0.05]) + self.src_scale_mod / 100.0), output_sample_types=[ [f.TRANSFORMED | f_type | f.MODE_BGR, self.resolution], [ f.TRANSFORMED | f_type | f.MODE_M | f.FACE_MASK_FULL, self.resolution ] ]), SampleGeneratorFace( self.training_data_dst_path, debug=self.is_debug(), batch_size=self.batch_size, sample_process_options=SampleProcessor.Options( random_flip=self.random_flip, normalize_tanh=True, scale_range=np.array([-0.05, 0.05]) + self.src_scale_mod / 100.0), output_sample_types=[[ f.TRANSFORMED | f_type | f.MODE_BGR, 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_on_batch([target_src], [target_src_mask]) return (('loss', loss), ) #override def onGetPreview(self, sample): test_A = sample[0][0][0:4] #first 4 samples test_B = sample[1][0][0:4] #first 4 samples mAA = self.fan_seg.extract_from_bgr([test_A]) mBB = self.fan_seg.extract_from_bgr([test_B]) 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], 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 [ ('FANSegmentator', np.concatenate(st, axis=0)), ('never seen', np.concatenate(st2, axis=0)), ] def predictor_func(self, face): face_64_bgr = face[..., 0:3] face_64_mask = np.expand_dims(face[..., 3], -1) x, mx = self.src_view([np.expand_dims(face_64_bgr, 0)]) x, mx = x[0], mx[0] return np.concatenate((x, mx), -1) #override def get_converter(self): from converters import ConverterMasked return ConverterMasked(self.predictor_func, predictor_input_size=64, output_size=64, face_type=FaceType.HALF, base_erode_mask_modifier=100, base_blur_mask_modifier=100)
class Model(ModelBase): def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs, ask_write_preview_history=False, ask_target_iter=False, ask_sort_by_yaw=False, ask_random_flip=False, ask_src_scale_mod=False) #override def onInitialize(self): exec(nnlib.import_all(), locals(), globals()) self.set_vram_batch_requirements({1.5: 4}) self.resolution = 256 self.face_type = FaceType.FULL self.fan_seg = FANSegmentator( 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: f = SampleProcessor.TypeFlags f_type = f.FACE_ALIGN_FULL 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, normalize_tanh=True), output_sample_types=[[ f.TRANSFORMED | f_type | f.MODE_BGR_SHUFFLE, self.resolution ], [ f.TRANSFORMED | f_type | f.MODE_M | f.FACE_MASK_FULL, 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, normalize_tanh=True), output_sample_types=[[ f.TRANSFORMED | f_type | f.MODE_BGR_SHUFFLE, 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_on_batch([target_src], [target_src_mask]) return (('loss', loss), ) #override def onGetPreview(self, sample): test_A = sample[0][0][0:4] #first 4 samples test_B = sample[1][0][0:4] #first 4 samples mAA = self.fan_seg.extract_from_bgr([test_A]) mBB = self.fan_seg.extract_from_bgr([test_B]) test_A, test_B, = [ np.clip((x + 1.0) / 2.0, 0.0, 1.0) for x in [test_A, test_B] ] 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], 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 [ ('FANSegmentator', np.concatenate(st, axis=0)), ('never seen', np.concatenate(st2, axis=0)), ]
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_sort_by_yaw=False, ask_random_flip=False, ask_src_scale_mod=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}) self.resolution = 256 self.face_type = FaceType.FULL if self.options[ 'face_type'] == 'f' else FaceType.HALF self.fan_seg = FANSegmentator( 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_SHUFFLE), 'resolution': self.resolution, 'motion_blur': (25, 1) }, { '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_SHUFFLE), '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_on_batch([target_src], [target_src_mask]) return (('loss', loss), ) #override def onGetPreview(self, sample): test_A = sample[0][0][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) mAA = np.repeat(mAA, (3, ), -1) mBB = np.repeat(mBB, (3, ), -1) st = [ np.concatenate(( test_A[i, :, :, 0:3], mAA[i], test_A[i, :, :, 0:3] * mAA[i], ), axis=1) for i in range(len(test_A)) ] st2 = [ np.concatenate(( test_B[i, :, :, 0:3], mBB[i], test_B[i, :, :, 0:3] * mBB[i], ), axis=1) for i in range(len(test_B)) ] return [ ('training data', np.concatenate(st, axis=0)), ('evaluating data', np.concatenate(st2, axis=0)), ]