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synced 2025-07-06 04:52:13 -07:00
fix xseg training
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iperov
parent
35945b257c
commit
254a7cf5cf
2 changed files with 62 additions and 62 deletions
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@ -29,7 +29,10 @@ class XSegNet(object):
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nn.initialize(data_format=data_format)
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tf = nn.tf
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model_name = f'{name}_{resolution}'
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self.model_filename_list = []
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with tf.device ('/CPU:0'):
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#Place holders on CPU
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self.input_t = tf.placeholder (nn.floatx, nn.get4Dshape(resolution,resolution,3) )
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@ -39,18 +42,17 @@ class XSegNet(object):
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with tf.device ('/CPU:0' if place_model_on_cpu else '/GPU:0'):
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self.model = nn.XSeg(3, 32, 1, name=name)
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self.model_weights = self.model.get_weights()
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if training:
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if optimizer is None:
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raise ValueError("Optimizer should be provided for training mode.")
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self.opt = optimizer
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self.opt.initialize_variables (self.model_weights, vars_on_cpu=place_model_on_cpu)
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self.model_filename_list += [ [self.opt, f'{model_name}_opt.npy' ] ]
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self.model_filename_list += [ [self.model, f'{model_name}.npy'] ]
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model_name = f'{name}_{resolution}'
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self.model_filename_list = [ [self.model, f'{model_name}.npy'] ]
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if training:
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if optimizer is None:
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raise ValueError("Optimizer should be provided for training mode.")
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self.opt = optimizer
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self.opt.initialize_variables (self.model_weights, vars_on_cpu=place_model_on_cpu)
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self.model_filename_list += [ [self.opt, f'{model_name}_opt.npy' ] ]
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else:
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if not training:
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with tf.device ('/CPU:0' if run_on_cpu else '/GPU:0'):
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_, pred = self.model(self.input_t)
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@ -15,23 +15,23 @@ class XSegModel(ModelBase):
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def __init__(self, *args, **kwargs):
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super().__init__(*args, force_model_class_name='XSeg', **kwargs)
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#override
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def on_initialize_options(self):
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def on_initialize_options(self):
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ask_override = self.ask_override()
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if not self.is_first_run() and ask_override:
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if not self.is_first_run() and ask_override:
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if io.input_bool(f"Restart training?", False, help_message="Reset model weights and start training from scratch."):
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self.set_iter(0)
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default_face_type = self.options['face_type'] = self.load_or_def_option('face_type', 'wf')
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if self.is_first_run():
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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. Choose the same as your deepfake model.").lower()
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if self.is_first_run() or ask_override:
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self.ask_batch_size(4, range=[2,16])
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self.ask_batch_size(4, range=[2,16])
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#override
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def on_initialize(self):
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device_config = nn.getCurrentDeviceConfig()
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@ -44,29 +44,29 @@ class XSegModel(ModelBase):
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self.resolution = resolution = 256
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self.face_type = {'h' : FaceType.HALF,
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'mf' : FaceType.MID_FULL,
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'f' : FaceType.FULL,
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'wf' : FaceType.WHOLE_FACE,
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'head' : FaceType.HEAD}[ self.options['face_type'] ]
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place_model_on_cpu = len(devices) == 0
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models_opt_device = '/CPU:0' if place_model_on_cpu else '/GPU:0'
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bgr_shape = nn.get4Dshape(resolution,resolution,3)
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mask_shape = nn.get4Dshape(resolution,resolution,1)
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# Initializing model classes
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self.model = XSegNet(name='XSeg',
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resolution=resolution,
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self.model = XSegNet(name='XSeg',
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resolution=resolution,
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load_weights=not self.is_first_run(),
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weights_file_root=self.get_model_root_path(),
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training=True,
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place_model_on_cpu=place_model_on_cpu,
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optimizer=nn.RMSprop(lr=0.0001, lr_dropout=0.3, name='opt'),
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data_format=nn.data_format)
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if self.is_training:
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# Adjust batch size for multiple GPU
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gpu_count = max(1, len(devices) )
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@ -79,20 +79,21 @@ class XSegModel(ModelBase):
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gpu_losses = []
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gpu_loss_gvs = []
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for gpu_id in range(gpu_count):
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with tf.device( f'/GPU:{gpu_id}' if len(devices) != 0 else f'/CPU:0' ):
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for gpu_id in range(gpu_count):
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with tf.device( f'/GPU:{gpu_id}' if len(devices) != 0 else f'/CPU:0' ):
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with tf.device(f'/CPU:0'):
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# slice on CPU, otherwise all batch data will be transfered to GPU first
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batch_slice = slice( gpu_id*bs_per_gpu, (gpu_id+1)*bs_per_gpu )
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gpu_input_t = self.model.input_t [batch_slice,:,:,:]
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gpu_target_t = self.model.target_t [batch_slice,:,:,:]
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gpu_target_t = self.model.target_t [batch_slice,:,:,:]
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# process model tensors
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gpu_pred_logits_t, gpu_pred_t = self.model.flow(gpu_input_t)
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gpu_pred_logits_t, gpu_pred_t = self.model.flow(gpu_input_t)
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gpu_pred_list.append(gpu_pred_t)
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gpu_loss = tf.reduce_mean( tf.nn.sigmoid_cross_entropy_with_logits(labels=gpu_target_t, logits=gpu_pred_logits_t), axis=[1,2,3])
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gpu_losses += [gpu_loss]
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@ -100,13 +101,13 @@ class XSegModel(ModelBase):
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# Average losses and gradients, and create optimizer update ops
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with tf.device (models_opt_device):
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pred = nn.concat(gpu_pred_list, 0)
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loss = tf.reduce_mean(gpu_losses)
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with tf.device(f'/CPU:0'):
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#with tf.device (models_opt_device):
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pred = tf.concat(gpu_pred_list, 0)
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loss = tf.concat(gpu_losses, 0)
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loss_gv_op = self.model.opt.get_update_op (nn.average_gv_list (gpu_loss_gvs))
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# Initializing training and view functions
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def train(input_np, target_np):
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l, _ = nn.tf_sess.run ( [loss, loss_gv_op], feed_dict={self.model.input_t :input_np, self.model.target_t :target_np })
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@ -122,29 +123,29 @@ class XSegModel(ModelBase):
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src_dst_generators_count = cpu_count // 2
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src_generators_count = cpu_count // 2
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dst_generators_count = cpu_count // 2
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srcdst_generator = SampleGeneratorFaceXSeg([self.training_data_src_path, self.training_data_dst_path],
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debug=self.is_debug(),
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batch_size=self.get_batch_size(),
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resolution=resolution,
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face_type=self.face_type,
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generators_count=src_dst_generators_count,
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generators_count=src_dst_generators_count,
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data_format=nn.data_format)
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src_generator = SampleGeneratorFace(self.training_data_src_path, debug=self.is_debug(), batch_size=self.get_batch_size(),
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sample_process_options=SampleProcessor.Options(random_flip=False),
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output_sample_types = [ {'sample_type': SampleProcessor.SampleType.FACE_IMAGE, 'warp':False, 'transform':False, 'channel_type' : SampleProcessor.ChannelType.BGR, 'border_replicate':False, 'face_type':self.face_type, 'data_format':nn.data_format, 'resolution': resolution},
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],
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generators_count=src_generators_count,
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raise_on_no_data=False )
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raise_on_no_data=False )
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dst_generator = SampleGeneratorFace(self.training_data_dst_path, debug=self.is_debug(), batch_size=self.get_batch_size(),
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sample_process_options=SampleProcessor.Options(random_flip=False),
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output_sample_types = [ {'sample_type': SampleProcessor.SampleType.FACE_IMAGE, 'warp':False, 'transform':False, 'channel_type' : SampleProcessor.ChannelType.BGR, 'border_replicate':False, 'face_type':self.face_type, 'data_format':nn.data_format, 'resolution': resolution},
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],
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generators_count=dst_generators_count,
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raise_on_no_data=False )
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self.set_training_data_generators ([srcdst_generator, src_generator, dst_generator])
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#override
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@ -154,21 +155,18 @@ class XSegModel(ModelBase):
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#override
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def onSave(self):
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self.model.save_weights()
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#override
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def onTrainOneIter(self):
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image_np, mask_np = self.generate_next_samples()[0]
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loss = self.train (image_np, mask_np)
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return ( ('loss', loss ), )
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#override
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def onTrainOneIter(self):
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image_np, mask_np = self.generate_next_samples()[0]
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loss = self.train (image_np, mask_np)
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return ( ('loss', np.mean(loss) ), )
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#override
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def onGetPreview(self, samples):
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n_samples = min(4, self.get_batch_size(), 800 // self.resolution )
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srcdst_samples, src_samples, dst_samples = samples
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srcdst_samples, src_samples, dst_samples = samples
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image_np, mask_np = srcdst_samples
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I, M, IM, = [ np.clip( nn.to_data_format(x,"NHWC", self.model_data_format), 0.0, 1.0) for x in ([image_np,mask_np] + self.view (image_np) ) ]
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@ -176,41 +174,41 @@ class XSegModel(ModelBase):
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green_bg = np.tile( np.array([0,1,0], dtype=np.float32)[None,None,...], (self.resolution,self.resolution,1) )
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result = []
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result = []
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st = []
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for i in range(n_samples):
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ar = I[i]*M[i]+0.5*I[i]*(1-M[i])+0.5*green_bg*(1-M[i]), IM[i], I[i]*IM[i]+0.5*I[i]*(1-IM[i]) + 0.5*green_bg*(1-IM[i])
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st.append ( np.concatenate ( ar, axis=1) )
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result += [ ('XSeg training faces', np.concatenate (st, axis=0 )), ]
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if len(src_samples) != 0:
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src_np, = src_samples
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D, DM, = [ np.clip(nn.to_data_format(x,"NHWC", self.model_data_format), 0.0, 1.0) for x in ([src_np] + self.view (src_np) ) ]
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DM, = [ np.repeat (x, (3,), -1) for x in [DM] ]
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st = []
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for i in range(n_samples):
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ar = D[i], DM[i], D[i]*DM[i] + 0.5*D[i]*(1-DM[i]) + 0.5*green_bg*(1-DM[i])
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st.append ( np.concatenate ( ar, axis=1) )
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result += [ ('XSeg src faces', np.concatenate (st, axis=0 )), ]
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if len(dst_samples) != 0:
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dst_np, = dst_samples
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D, DM, = [ np.clip(nn.to_data_format(x,"NHWC", self.model_data_format), 0.0, 1.0) for x in ([dst_np] + self.view (dst_np) ) ]
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DM, = [ np.repeat (x, (3,), -1) for x in [DM] ]
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st = []
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for i in range(n_samples):
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ar = D[i], DM[i], D[i]*DM[i] + 0.5*D[i]*(1-DM[i]) + 0.5*green_bg*(1-DM[i])
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st.append ( np.concatenate ( ar, axis=1) )
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result += [ ('XSeg dst faces', np.concatenate (st, axis=0 )), ]
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return result
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Model = XSegModel
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