mirror of
https://github.com/iperov/DeepFaceLab.git
synced 2025-07-06 13:02:15 -07:00
added '?' help for model options. Added 'Src face scale modifier' to model options.
This commit is contained in:
iperov
parent
e8620919a7
commit
1a2555e160
8 changed files with 254 additions and 104 deletions
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@ -92,7 +92,9 @@ DF - good for side faces, but results in a lower resolution and details. Covers
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LIAE - can partially fix dissimilar face shapes, but results in a less recognizable face.
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SAE - no matter how similar faces, src face will be morphed onto dst face, which can make face absolutely unrecognizable. Model can collapse on some scenes. Easy to overlay final face because dst background is also predicted.
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also quality of src faceset significantly affects the final face.
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Quality of src faceset significantly affects the final face.
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Narrow src face is better fakeable than wide. This is why Cage is so popular in deepfakes.
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### **Sort tool**:
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@ -58,11 +58,12 @@ class ModelBase(object):
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if self.epoch == 0:
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print ("\nModel first run. Enter model options as default for each run.")
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self.options['write_preview_history'] = input_bool("Write preview history? (y/n skip:n) : ", False)
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self.options['write_preview_history'] = input_bool("Write preview history? (y/n ?:help skip:n) : ", False, help_message="Preview history will be writed to <ModelName>_history folder.")
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self.options['target_epoch'] = max(0, input_int("Target epoch (skip:unlimited) : ", 0))
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self.options['batch_size'] = max(0, input_int("Batch_size (skip:model choice) : ", 0))
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self.options['sort_by_yaw'] = input_bool("Feed faces to network sorted by yaw? (y/n skip:n) : ", False)
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self.options['random_flip'] = input_bool("Flip faces randomly? (y/n skip:y) : ", True)
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self.options['batch_size'] = max(0, input_int("Batch_size (?:help skip:model choice) : ", 0, help_message="Larger batch size is always better for NN's generalization, but it can cause Out of Memory error. Tune this value for your videocard manually."))
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self.options['sort_by_yaw'] = input_bool("Feed faces to network sorted by yaw? (y/n ?:help skip:n) : ", False, help_message="NN will not learn src face directions that don't match dst face directions." )
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self.options['random_flip'] = input_bool("Flip faces randomly? (y/n ?:help skip:y) : ", True, help_message="Predicted face will look more naturally without this option, but src faceset should cover all face directions as dst faceset.")
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self.options['src_scale_mod'] = np.clip( input_int("Src face scale modifier % ( -30...30, ?:help skip:0) : ", 0, help_message="If src face shape is wider than dst, try to decrease this value to get a better result."), -30, 30)
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#self.options['use_fp16'] = use_fp16 = input_bool("Use float16? (y/n skip:n) : ", False)
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else:
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self.options['write_preview_history'] = self.options.get('write_preview_history', False)
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@ -70,6 +71,7 @@ class ModelBase(object):
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self.options['batch_size'] = self.options.get('batch_size', 0)
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self.options['sort_by_yaw'] = self.options.get('sort_by_yaw', False)
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self.options['random_flip'] = self.options.get('random_flip', True)
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self.options['src_scale_mod'] = self.options.get('src_scale_mod', 0)
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#self.options['use_fp16'] = use_fp16 = self.options['use_fp16'] if 'use_fp16' in self.options.keys() else False
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use_fp16 = False #currently models fails with fp16
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@ -105,6 +107,10 @@ class ModelBase(object):
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self.random_flip = self.options['random_flip']
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if self.random_flip:
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self.options.pop('random_flip')
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self.src_scale_mod = self.options['src_scale_mod']
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if self.src_scale_mod == 0:
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self.options.pop('src_scale_mod')
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self.write_preview_history = session_write_preview_history
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self.target_epoch = session_target_epoch
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@ -40,7 +40,7 @@ class Model(ModelBase):
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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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debug=self.is_debug(), batch_size=self.batch_size,
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sample_process_options=SampleProcessor.Options(random_flip=self.random_flip),
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sample_process_options=SampleProcessor.Options(random_flip=self.random_flip, scale_range=np.array([-0.05, 0.05])+self.src_scale_mod / 100.0 ),
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output_sample_types=[ [f.WARPED_TRANSFORMED | f.FACE_ALIGN_FULL | f.MODE_BGR, 128],
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[f.TRANSFORMED | f.FACE_ALIGN_FULL | f.MODE_BGR, 128],
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[f.TRANSFORMED | f.FACE_ALIGN_FULL | f.MODE_M | f.FACE_MASK_FULL, 128] ] ),
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@ -46,7 +46,7 @@ class Model(ModelBase):
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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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debug=self.is_debug(), batch_size=self.batch_size,
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sample_process_options=SampleProcessor.Options(random_flip=self.random_flip),
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sample_process_options=SampleProcessor.Options(random_flip=self.random_flip, scale_range=np.array([-0.05, 0.05])+self.src_scale_mod / 100.0 ),
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output_sample_types=[ [f.WARPED_TRANSFORMED | f.FACE_ALIGN_HALF | f.MODE_BGR, 128],
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[f.TRANSFORMED | f.FACE_ALIGN_HALF | f.MODE_BGR, 128],
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[f.TRANSFORMED | f.FACE_ALIGN_HALF | f.MODE_M | f.FACE_MASK_FULL, 128] ] ),
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@ -47,7 +47,7 @@ class Model(ModelBase):
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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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debug=self.is_debug(), batch_size=self.batch_size,
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sample_process_options=SampleProcessor.Options(random_flip=self.random_flip),
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sample_process_options=SampleProcessor.Options(random_flip=self.random_flip, scale_range=np.array([-0.05, 0.05])+self.src_scale_mod / 100.0 ),
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output_sample_types=[ [f.WARPED_TRANSFORMED | f.FACE_ALIGN_HALF | f.MODE_BGR, 64],
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[f.TRANSFORMED | f.FACE_ALIGN_HALF | f.MODE_BGR, 64],
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[f.TRANSFORMED | f.FACE_ALIGN_HALF | f.MODE_M | f.FACE_MASK_FULL, 64] ] ),
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@ -47,7 +47,7 @@ class Model(ModelBase):
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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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debug=self.is_debug(), batch_size=self.batch_size,
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sample_process_options=SampleProcessor.Options(random_flip=self.random_flip),
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sample_process_options=SampleProcessor.Options(random_flip=self.random_flip, scale_range=np.array([-0.05, 0.05])+self.src_scale_mod / 100.0 ),
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output_sample_types=[ [f.WARPED_TRANSFORMED | f.FACE_ALIGN_FULL | f.MODE_BGR, 128],
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[f.TRANSFORMED | f.FACE_ALIGN_FULL | f.MODE_BGR, 128],
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[f.TRANSFORMED | f.FACE_ALIGN_FULL | f.MODE_M | f.FACE_MASK_FULL, 128] ] ),
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@ -15,21 +15,32 @@ class SAEModel(ModelBase):
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decoderH5 = 'decoder.h5'
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decodermH5 = 'decoderm.h5'
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decoder_srcH5 = 'decoder_src.h5'
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decoder_srcmH5 = 'decoder_srcm.h5'
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decoder_dstH5 = 'decoder_dst.h5'
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decoder_dstmH5 = 'decoder_dstm.h5'
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#override
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def onInitializeOptions(self, is_first_run, ask_for_session_options):
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default_resolution = 128
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default_ae_dims = 256
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default_archi = 'liae'
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default_style_power = 100
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default_face_type = 'f'
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if is_first_run:
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#first run
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self.options['resolution'] = input_int("Resolution (valid: 64,128, skip:128) : ", default_resolution, [64,128])
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self.options['ae_dims'] = input_int("AutoEncoder dims (valid: 128,256,512 skip:256) : ", default_ae_dims, [128,256,512])
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self.options['face_type'] = input_str ("Half or Full face? (h/f, skip:f) : ", default_face_type, ['h','f'])
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self.options['resolution'] = input_int("Resolution (64,128, ?:help skip:128) : ", default_resolution, [64,128], help_message="More resolution requires more VRAM.")
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self.options['archi'] = input_str ("AE architecture (df, liae, ?:help skip:liae) : ", default_archi, ['df','liae'], help_message="DF keeps faces more natural, while LIAE can fix overly different face shapes.").lower()
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self.options['style_power'] = np.clip ( input_int("Style power (1..100 ?:help skip:100) : ", default_style_power, help_message="How fast NN will learn dst style during generalization of src and dst faces."), 1, 100 )
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default_ae_dims = 256 if self.options['archi'] == 'liae' else 512
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self.options['ae_dims'] = input_int("AutoEncoder dims (128,256,512 ?:help skip:%d) : " % (default_ae_dims) , default_ae_dims, [128,256,512], help_message="More dims are better, but requires more VRAM." )
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self.options['face_type'] = 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()
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else:
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#not first run
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self.options['resolution'] = self.options.get('resolution', default_resolution)
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self.options['archi'] = self.options.get('archi', default_archi)
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self.options['style_power'] = self.options.get('style_power', default_style_power)
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default_ae_dims = 256 if self.options['archi'] == 'liae' else 512
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self.options['ae_dims'] = self.options.get('ae_dims', default_ae_dims)
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self.options['face_type'] = self.options.get('face_type', default_face_type)
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@ -44,52 +55,84 @@ class SAEModel(ModelBase):
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bgr_shape = (resolution, resolution, 3)
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mask_shape = (resolution, resolution, 1)
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self.encoder = modelify(SAEModel.EncFlow() ) (Input(bgr_shape))
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enc_output_Inputs = [ Input(K.int_shape(x)[1:]) for x in self.encoder.outputs ]
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self.inter_B = modelify(SAEModel.InterFlow(dims=ae_dims,lowest_dense_res=resolution // 16)) (enc_output_Inputs)
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self.inter_AB = modelify(SAEModel.InterFlow(dims=ae_dims,lowest_dense_res=resolution // 16)) (enc_output_Inputs)
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inter_output_Inputs = [ Input( np.array(K.int_shape(x)[1:])*(1,1,2) ) for x in self.inter_B.outputs ]
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self.decoder = modelify(SAEModel.DecFlow (bgr_shape[2],dims=ae_dims*2)) (inter_output_Inputs)
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self.decoderm = modelify(SAEModel.DecFlow (mask_shape[2],dims=ae_dims)) (inter_output_Inputs)
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if not self.is_first_run():
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self.encoder.load_weights (self.get_strpath_storage_for_file(self.encoderH5))
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self.inter_B.load_weights (self.get_strpath_storage_for_file(self.inter_BH5))
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self.inter_AB.load_weights (self.get_strpath_storage_for_file(self.inter_ABH5))
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self.decoder.load_weights (self.get_strpath_storage_for_file(self.decoderH5))
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self.decoderm.load_weights (self.get_strpath_storage_for_file(self.decodermH5))
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warped_src = Input(bgr_shape)
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target_src = Input(bgr_shape)
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target_srcm = Input(mask_shape)
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warped_src_code = self.encoder (warped_src)
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warped_src_inter_AB_code = self.inter_AB (warped_src_code)
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warped_src_inter_code = Concatenate()([warped_src_inter_AB_code,warped_src_inter_AB_code])
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pred_src_src = self.decoder(warped_src_inter_code)
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pred_src_srcm = self.decoderm(warped_src_inter_code)
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warped_dst = Input(bgr_shape)
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target_dst = Input(bgr_shape)
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target_dstm = Input(mask_shape)
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if self.options['archi'] == 'liae':
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self.encoder = modelify(SAEModel.EncFlow() ) (Input(bgr_shape))
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enc_output_Inputs = [ Input(K.int_shape(x)[1:]) for x in self.encoder.outputs ]
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self.inter_B = modelify(SAEModel.InterFlow(dims=ae_dims,lowest_dense_res=resolution // 16)) (enc_output_Inputs)
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self.inter_AB = modelify(SAEModel.InterFlow(dims=ae_dims,lowest_dense_res=resolution // 16)) (enc_output_Inputs)
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inter_output_Inputs = [ Input( np.array(K.int_shape(x)[1:])*(1,1,2) ) for x in self.inter_B.outputs ]
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self.decoder = modelify(SAEModel.DecFlow (bgr_shape[2],dims=ae_dims*2)) (inter_output_Inputs)
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self.decoderm = modelify(SAEModel.DecFlow (mask_shape[2],dims=ae_dims)) (inter_output_Inputs)
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if not self.is_first_run():
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self.encoder.load_weights (self.get_strpath_storage_for_file(self.encoderH5))
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self.inter_B.load_weights (self.get_strpath_storage_for_file(self.inter_BH5))
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self.inter_AB.load_weights (self.get_strpath_storage_for_file(self.inter_ABH5))
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self.decoder.load_weights (self.get_strpath_storage_for_file(self.decoderH5))
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self.decoderm.load_weights (self.get_strpath_storage_for_file(self.decodermH5))
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warped_src_code = self.encoder (warped_src)
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warped_src_inter_AB_code = self.inter_AB (warped_src_code)
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warped_src_inter_code = Concatenate()([warped_src_inter_AB_code,warped_src_inter_AB_code])
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pred_src_src = self.decoder(warped_src_inter_code)
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pred_src_srcm = self.decoderm(warped_src_inter_code)
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warped_dst_code = self.encoder (warped_dst)
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warped_dst_inter_B_code = self.inter_B (warped_dst_code)
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warped_dst_inter_AB_code = self.inter_AB (warped_dst_code)
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warped_dst_inter_code = Concatenate()([warped_dst_inter_B_code,warped_dst_inter_AB_code])
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pred_dst_dst = self.decoder(warped_dst_inter_code)
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pred_dst_dstm = self.decoderm(warped_dst_inter_code)
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warped_src_dst_inter_code = Concatenate()([warped_dst_inter_AB_code,warped_dst_inter_AB_code])
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pred_src_dst = self.decoder(warped_src_dst_inter_code)
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pred_src_dstm = self.decoderm(warped_src_dst_inter_code)
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else:
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self.encoder = modelify(SAEModel.DFEncFlow(dims=ae_dims,lowest_dense_res=resolution // 16) ) (Input(bgr_shape))
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dec_Inputs = [ Input(K.int_shape(x)[1:]) for x in self.encoder.outputs ]
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self.decoder_src = modelify(SAEModel.DFDecFlow (bgr_shape[2],dims=ae_dims)) (dec_Inputs)
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self.decoder_dst = modelify(SAEModel.DFDecFlow (bgr_shape[2],dims=ae_dims)) (dec_Inputs)
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self.decoder_srcm = modelify(SAEModel.DFDecFlow (mask_shape[2],dims=ae_dims//2)) (dec_Inputs)
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self.decoder_dstm = modelify(SAEModel.DFDecFlow (mask_shape[2],dims=ae_dims//2)) (dec_Inputs)
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warped_dst_code = self.encoder (warped_dst)
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warped_dst_inter_B_code = self.inter_B (warped_dst_code)
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warped_dst_inter_AB_code = self.inter_AB (warped_dst_code)
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warped_dst_inter_code = Concatenate()([warped_dst_inter_B_code,warped_dst_inter_AB_code])
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pred_dst_dst = self.decoder(warped_dst_inter_code)
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pred_dst_dstm = self.decoderm(warped_dst_inter_code)
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warped_src_dst_inter_code = Concatenate()([warped_dst_inter_AB_code,warped_dst_inter_AB_code])
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pred_src_dst = self.decoder(warped_src_dst_inter_code)
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pred_src_dstm = self.decoderm(warped_src_dst_inter_code)
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if not self.is_first_run():
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self.encoder.load_weights (self.get_strpath_storage_for_file(self.encoderH5))
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self.decoder_src.load_weights (self.get_strpath_storage_for_file(self.decoder_srcH5))
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self.decoder_srcm.load_weights (self.get_strpath_storage_for_file(self.decoder_srcmH5))
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self.decoder_dst.load_weights (self.get_strpath_storage_for_file(self.decoder_dstH5))
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self.decoder_dstm.load_weights (self.get_strpath_storage_for_file(self.decoder_dstmH5))
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warped_src_code = self.encoder (warped_src)
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pred_src_src = self.decoder_src(warped_src_code)
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pred_src_srcm = self.decoder_srcm(warped_src_code)
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warped_dst_code = self.encoder (warped_dst)
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pred_dst_dst = self.decoder_dst(warped_dst_code)
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pred_dst_dstm = self.decoder_dstm(warped_dst_code)
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pred_src_dst = self.decoder_src(warped_dst_code)
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pred_src_dstm = self.decoder_srcm(warped_dst_code)
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target_srcm_blurred = tf_gaussian_blur(resolution // 32)(target_srcm)
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target_srcm_sigm = target_srcm_blurred / 2.0 + 0.5
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target_srcm_anti_sigm = 1.0 - target_srcm_sigm
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@ -105,48 +148,61 @@ class SAEModel(ModelBase):
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pred_dst_dst_sigm = pred_dst_dst+1
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pred_src_dst_sigm = pred_src_dst+1
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pred_src_dstm_blurred = tf_gaussian_blur(resolution // 32)(pred_src_dstm)
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pred_src_dstm_sigm = pred_src_dstm_blurred / 2.0 + 0.5
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pred_src_dstm_anti_sigm = 1.0 - pred_src_dstm_sigm
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target_src_masked = target_src_sigm*target_srcm_sigm
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target_dst_masked = target_dst_sigm * target_dstm_sigm
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target_dst_anti_masked = target_dst_sigm * target_dstm_anti_sigm
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target_dst_psd_masked = target_dst_sigm * pred_src_dstm_sigm
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target_dst_psd_anti_masked = target_dst_sigm * pred_src_dstm_anti_sigm
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pred_src_src_masked = pred_src_src_sigm * target_srcm_sigm
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pred_dst_dst_masked = pred_dst_dst_sigm * target_dstm_sigm
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psd_target_dst_masked = pred_src_dst_sigm * target_dstm_sigm
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psd_target_dst_anti_masked = pred_src_dst_sigm * target_dstm_anti_sigm
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psd_psd_masked = pred_src_dst_sigm * pred_src_dstm_sigm
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psd_psd_anti_masked = pred_src_dst_sigm * pred_src_dstm_anti_sigm
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style_power = self.options['style_power'] / 100.0
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src_loss = K.mean( 100*K.square(tf_dssim(2.0)( target_src_masked, pred_src_src_masked )) )
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src_loss += tf_style_loss(gaussian_blur_radius=resolution // 8, loss_weight=0.2)(psd_target_dst_masked, target_dst_masked)
|
||||
src_loss += K.mean( 100*K.square(tf_dssim(2.0)( psd_target_dst_anti_masked, target_dst_anti_masked )))
|
||||
#src_loss += tf_style_loss(gaussian_blur_radius=resolution // 8, loss_weight=0.2)(psd_psd_masked, target_dst_psd_masked)
|
||||
#src_loss += K.mean( 100*K.square(tf_dssim(2.0)( psd_psd_anti_masked, target_dst_psd_anti_masked )))
|
||||
#src_loss += tf_style_loss(gaussian_blur_radius=resolution // 8, loss_weight=0.2*style_power)(psd_target_dst_masked, target_dst_masked)
|
||||
src_loss += K.mean( (100*style_power)*K.square(tf_dssim(2.0)( psd_target_dst_anti_masked, target_dst_anti_masked )))
|
||||
|
||||
if self.options['archi'] == 'liae':
|
||||
src_train_weights = self.encoder.trainable_weights + self.inter_AB.trainable_weights + self.decoder.trainable_weights
|
||||
else:
|
||||
src_train_weights = self.encoder.trainable_weights + self.decoder_src.trainable_weights
|
||||
self.src_train = K.function ([warped_src, target_src, target_srcm, warped_dst, target_dst, target_dstm ],[src_loss],
|
||||
Adam(lr=5e-5, beta_1=0.5, beta_2=0.999).get_updates(src_loss, self.encoder.trainable_weights + self.inter_AB.trainable_weights + self.decoder.trainable_weights) )
|
||||
|
||||
Adam(lr=5e-5, beta_1=0.5, beta_2=0.999).get_updates(src_loss, src_train_weights) )
|
||||
|
||||
|
||||
dst_loss = K.mean( 100*K.square(tf_dssim(2.0)( target_dst_masked, pred_dst_dst_masked )) )
|
||||
|
||||
if self.options['archi'] == 'liae':
|
||||
dst_train_weights = self.encoder.trainable_weights + self.inter_B.trainable_weights + self.inter_AB.trainable_weights + self.decoder.trainable_weights
|
||||
else:
|
||||
dst_train_weights = self.encoder.trainable_weights + self.decoder_dst.trainable_weights
|
||||
self.dst_train = K.function ([warped_dst, target_dst, target_dstm],[dst_loss],
|
||||
Adam(lr=5e-5, beta_1=0.5, beta_2=0.999).get_updates(dst_loss, self.encoder.trainable_weights + self.inter_B.trainable_weights + self.inter_AB.trainable_weights + self.decoder.trainable_weights) )
|
||||
Adam(lr=5e-5, beta_1=0.5, beta_2=0.999).get_updates(dst_loss, dst_train_weights) )
|
||||
|
||||
|
||||
src_mask_loss = K.mean(K.square(target_srcm-pred_src_srcm))
|
||||
src_mask_loss = K.mean(K.square(target_srcm-pred_src_srcm))
|
||||
|
||||
if self.options['archi'] == 'liae':
|
||||
src_mask_train_weights = self.encoder.trainable_weights + self.inter_AB.trainable_weights + self.decoderm.trainable_weights
|
||||
else:
|
||||
src_mask_train_weights = self.encoder.trainable_weights + self.decoder_srcm.trainable_weights
|
||||
|
||||
self.src_mask_train = K.function ([warped_src, target_srcm],[src_mask_loss],
|
||||
Adam(lr=5e-5, beta_1=0.5, beta_2=0.999).get_updates(src_mask_loss, self.encoder.trainable_weights + self.inter_AB.trainable_weights + self.decoderm.trainable_weights) )
|
||||
Adam(lr=5e-5, beta_1=0.5, beta_2=0.999).get_updates(src_mask_loss, src_mask_train_weights ) )
|
||||
|
||||
dst_mask_loss = K.mean(K.square(target_dstm-pred_dst_dstm))
|
||||
dst_mask_loss = K.mean(K.square(target_dstm-pred_dst_dstm))
|
||||
|
||||
if self.options['archi'] == 'liae':
|
||||
dst_mask_train_weights = self.encoder.trainable_weights + self.inter_B.trainable_weights + self.inter_AB.trainable_weights + self.decoderm.trainable_weights
|
||||
else:
|
||||
dst_mask_train_weights = self.encoder.trainable_weights + self.decoder_dstm.trainable_weights
|
||||
|
||||
self.dst_mask_train = K.function ([warped_dst, target_dstm],[dst_mask_loss],
|
||||
Adam(lr=5e-5, beta_1=0.5, beta_2=0.999).get_updates(dst_mask_loss, self.encoder.trainable_weights + self.inter_B.trainable_weights + self.inter_AB.trainable_weights + self.decoderm.trainable_weights) )
|
||||
Adam(lr=5e-5, beta_1=0.5, beta_2=0.999).get_updates(dst_mask_loss, dst_mask_train_weights) )
|
||||
|
||||
self.AE_view = K.function ([warped_src, warped_dst],[pred_src_src, pred_src_srcm, pred_dst_dst, pred_dst_dstm, pred_src_dst, pred_src_dstm])
|
||||
self.AE_convert = K.function ([warped_dst],[pred_src_dst, pred_src_dstm])
|
||||
|
|
@ -155,11 +211,11 @@ class SAEModel(ModelBase):
|
|||
f = SampleProcessor.TypeFlags
|
||||
|
||||
face_type = f.FACE_ALIGN_FULL if self.options['face_type'] == 'f' else f.FACE_ALIGN_HALF
|
||||
|
||||
|
||||
self.set_training_data_generators ([
|
||||
SampleGeneratorFace(self.training_data_src_path, sort_by_yaw_target_samples_path=self.training_data_dst_path if self.sort_by_yaw else None,
|
||||
debug=self.is_debug(), batch_size=self.batch_size,
|
||||
sample_process_options=SampleProcessor.Options(random_flip=self.random_flip, normalize_tanh = True),
|
||||
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.WARPED_TRANSFORMED | face_type | f.MODE_BGR, resolution],
|
||||
[f.TRANSFORMED | face_type | f.MODE_BGR, resolution],
|
||||
[f.TRANSFORMED | face_type | f.MODE_M | f.FACE_MASK_FULL, resolution],
|
||||
|
|
@ -174,14 +230,22 @@ class SAEModel(ModelBase):
|
|||
[f.TRANSFORMED | face_type | f.MODE_M | f.FACE_MASK_FULL, resolution] ] )
|
||||
])
|
||||
#override
|
||||
def onSave(self):
|
||||
self.save_weights_safe( [[self.encoder, self.get_strpath_storage_for_file(self.encoderH5)],
|
||||
[self.inter_B, self.get_strpath_storage_for_file(self.inter_BH5)],
|
||||
[self.inter_AB, self.get_strpath_storage_for_file(self.inter_ABH5)],
|
||||
[self.decoder, self.get_strpath_storage_for_file(self.decoderH5)],
|
||||
[self.decoderm, self.get_strpath_storage_for_file(self.decodermH5)],
|
||||
] )
|
||||
|
||||
def onSave(self):
|
||||
if self.options['archi'] == 'liae':
|
||||
self.save_weights_safe( [[self.encoder, self.get_strpath_storage_for_file(self.encoderH5)],
|
||||
[self.inter_B, self.get_strpath_storage_for_file(self.inter_BH5)],
|
||||
[self.inter_AB, self.get_strpath_storage_for_file(self.inter_ABH5)],
|
||||
[self.decoder, self.get_strpath_storage_for_file(self.decoderH5)],
|
||||
[self.decoderm, self.get_strpath_storage_for_file(self.decodermH5)],
|
||||
] )
|
||||
else:
|
||||
self.save_weights_safe( [[self.encoder, self.get_strpath_storage_for_file(self.encoderH5)],
|
||||
[self.decoder_src, self.get_strpath_storage_for_file(self.decoder_srcH5)],
|
||||
[self.decoder_srcm, self.get_strpath_storage_for_file(self.decoder_srcmH5)],
|
||||
[self.decoder_dst, self.get_strpath_storage_for_file(self.decoder_dstH5)],
|
||||
[self.decoder_dstm, self.get_strpath_storage_for_file(self.decoder_dstmH5)],
|
||||
] )
|
||||
|
||||
#override
|
||||
def onTrainOneEpoch(self, sample):
|
||||
warped_src, target_src, target_src_mask = sample[0]
|
||||
|
|
@ -216,7 +280,7 @@ class SAEModel(ModelBase):
|
|||
st.append ( np.concatenate ( (
|
||||
S[i], SS[i], #SM[i],
|
||||
D[i], DD[i], #DM[i],
|
||||
SD[i], #SDM[i]
|
||||
SD[i], SDM[i]
|
||||
), axis=1) )
|
||||
|
||||
return [ ('SAE', np.concatenate ( st, axis=0 ) ) ]
|
||||
|
|
@ -306,4 +370,54 @@ class SAEModel(ModelBase):
|
|||
|
||||
return func
|
||||
|
||||
|
||||
@staticmethod
|
||||
def DFEncFlow(dims=512, lowest_dense_res=8):
|
||||
exec (nnlib.import_all(), locals(), globals())
|
||||
|
||||
def downscale (dim):
|
||||
def func(x):
|
||||
return LeakyReLU(0.1)(Conv2D(dim, 5, strides=2, padding='same')(x))
|
||||
return func
|
||||
|
||||
def upscale (dim):
|
||||
def func(x):
|
||||
return SubpixelUpscaler()(LeakyReLU(0.1)(Conv2D(dim * 4, 3, strides=1, padding='same')(x)))
|
||||
return func
|
||||
|
||||
def func(input):
|
||||
x = input
|
||||
|
||||
x = downscale(128)(x)
|
||||
x = downscale(256)(x)
|
||||
x = downscale(512)(x)
|
||||
x = downscale(1024)(x)
|
||||
|
||||
x = Dense(dims)(Flatten()(x))
|
||||
x = Dense(lowest_dense_res * lowest_dense_res * dims)(x)
|
||||
x = Reshape((lowest_dense_res, lowest_dense_res, dims))(x)
|
||||
x = upscale(dims)(x)
|
||||
|
||||
return x
|
||||
return func
|
||||
|
||||
@staticmethod
|
||||
def DFDecFlow(output_nc,dims,activation='tanh'):
|
||||
exec (nnlib.import_all(), locals(), globals())
|
||||
|
||||
def upscale (dim):
|
||||
def func(x):
|
||||
return SubpixelUpscaler()(LeakyReLU(0.1)(Conv2D(dim * 4, 3, strides=1, padding='same')(x)))
|
||||
return func
|
||||
def func(input):
|
||||
x = input[0]
|
||||
x = upscale(dims)(x)
|
||||
x = upscale(dims//2)(x)
|
||||
x = upscale(dims//4)(x)
|
||||
|
||||
x = Conv2D(output_nc, kernel_size=5, padding='same', activation=activation)(x)
|
||||
return x
|
||||
|
||||
return func
|
||||
|
||||
Model = SAEModel
|
||||
|
|
@ -1,26 +1,54 @@
|
|||
|
||||
|
||||
def input_int(s, default_value, valid_list=None):
|
||||
try:
|
||||
inp = input(s)
|
||||
i = int(inp)
|
||||
if (valid_list is not None) and (i not in valid_list):
|
||||
def input_int(s, default_value, valid_list=None, help_message=None):
|
||||
while True:
|
||||
try:
|
||||
inp = input(s)
|
||||
if len(inp) == 0:
|
||||
raise ValueError("")
|
||||
|
||||
if help_message is not None and inp == '?':
|
||||
print (help_message)
|
||||
continue
|
||||
|
||||
i = int(inp)
|
||||
if (valid_list is not None) and (i not in valid_list):
|
||||
return default_value
|
||||
return i
|
||||
except:
|
||||
print (default_value)
|
||||
return default_value
|
||||
return i
|
||||
except:
|
||||
return default_value
|
||||
|
||||
def input_bool(s, default_value):
|
||||
try:
|
||||
return bool ( {"y":True,"n":False,"1":True,"0":False}.get(input(s).lower(), default_value) )
|
||||
except:
|
||||
return default_value
|
||||
|
||||
def input_str(s, default_value, valid_list=None):
|
||||
try:
|
||||
inp = input(s)
|
||||
if (valid_list is not None) and (inp.lower() not in valid_list):
|
||||
def input_bool(s, default_value, help_message=None):
|
||||
while True:
|
||||
try:
|
||||
inp = input(s)
|
||||
if len(inp) == 0:
|
||||
raise ValueError("")
|
||||
|
||||
if help_message is not None and inp == '?':
|
||||
print (help_message)
|
||||
continue
|
||||
|
||||
return bool ( {"y":True,"n":False,"1":True,"0":False}.get(inp.lower(), default_value) )
|
||||
except:
|
||||
print ( "y" if default_value else "n" )
|
||||
return default_value
|
||||
return inp
|
||||
except:
|
||||
return default_value
|
||||
|
||||
def input_str(s, default_value, valid_list=None, help_message=None):
|
||||
while True:
|
||||
try:
|
||||
inp = input(s)
|
||||
if len(inp) == 0:
|
||||
raise ValueError("")
|
||||
|
||||
if help_message is not None and inp == '?':
|
||||
print (help_message)
|
||||
continue
|
||||
|
||||
if (valid_list is not None) and (inp.lower() not in valid_list):
|
||||
return default_value
|
||||
return inp
|
||||
except:
|
||||
print (default_value)
|
||||
return default_value
|
||||
Loading…
Add table
Add a link
Reference in a new issue