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synced 2025-03-12 20:42:45 -07:00
XSeg: added pretrain option.
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iperov
parent
83b1412da7
commit
55b947eab5
@ -88,9 +88,9 @@ class XSeg(nn.ModelBase):
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self.uconv02 = ConvBlock(base_ch*2, base_ch)
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self.uconv01 = ConvBlock(base_ch, base_ch)
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self.out_conv = nn.Conv2D (base_ch, out_ch, kernel_size=3, padding='SAME')
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def forward(self, inp):
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def forward(self, inp, pretrain=False):
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x = inp
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x = self.conv01(x)
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@ -126,29 +126,41 @@ class XSeg(nn.ModelBase):
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x = nn.reshape_4D (x, 4, 4, self.base_ch*8 )
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x = self.up5(x)
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if pretrain:
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x5 = tf.zeros_like(x5)
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x = self.uconv53(tf.concat([x,x5],axis=nn.conv2d_ch_axis))
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x = self.uconv52(x)
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x = self.uconv51(x)
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x = self.up4(x)
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if pretrain:
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x4 = tf.zeros_like(x4)
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x = self.uconv43(tf.concat([x,x4],axis=nn.conv2d_ch_axis))
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x = self.uconv42(x)
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x = self.uconv41(x)
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x = self.up3(x)
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if pretrain:
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x3 = tf.zeros_like(x3)
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x = self.uconv33(tf.concat([x,x3],axis=nn.conv2d_ch_axis))
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x = self.uconv32(x)
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x = self.uconv31(x)
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x = self.up2(x)
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if pretrain:
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x2 = tf.zeros_like(x2)
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x = self.uconv22(tf.concat([x,x2],axis=nn.conv2d_ch_axis))
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x = self.uconv21(x)
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x = self.up1(x)
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if pretrain:
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x1 = tf.zeros_like(x1)
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x = self.uconv12(tf.concat([x,x1],axis=nn.conv2d_ch_axis))
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x = self.uconv11(x)
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x = self.up0(x)
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if pretrain:
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x0 = tf.zeros_like(x0)
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x = self.uconv02(tf.concat([x,x0],axis=nn.conv2d_ch_axis))
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x = self.uconv01(x)
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@ -81,8 +81,8 @@ class XSegNet(object):
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def get_resolution(self):
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return self.resolution
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def flow(self, x):
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return self.model(x)
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def flow(self, x, pretrain=False):
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return self.model(x, pretrain=pretrain)
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def get_weights(self):
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return self.model_weights
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@ -25,13 +25,20 @@ class XSegModel(ModelBase):
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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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default_pretrain = self.options['pretrain'] = self.load_or_def_option('pretrain', False)
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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.options['pretrain'] = io.input_bool ("Enable pretraining mode", default_pretrain)
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if self.options['pretrain'] and self.get_pretraining_data_path() is None:
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raise Exception("pretraining_data_path is not defined")
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self.pretrain_just_disabled = (default_pretrain == True and self.options['pretrain'] == False)
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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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@ -50,7 +57,8 @@ class XSegModel(ModelBase):
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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 nn.tf_default_device_name
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@ -66,14 +74,19 @@ class XSegModel(ModelBase):
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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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self.pretrain = self.options['pretrain']
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if self.pretrain_just_disabled:
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self.set_iter(0)
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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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bs_per_gpu = max(1, self.get_batch_size() // gpu_count)
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self.set_batch_size( gpu_count*bs_per_gpu)
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targetm_t = tf.placeholder (nn.floatx, mask_shape)
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# Compute losses per GPU
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gpu_pred_list = []
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@ -81,20 +94,33 @@ class XSegModel(ModelBase):
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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'/{devices[gpu_id].tf_dev_type}:{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_targetm_t = targetm_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, pretrain=self.pretrain)
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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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if self.pretrain:
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gpu_targetm_blur = nn.gaussian_blur(gpu_targetm_t, max(1, resolution // 32) )
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gpu_targetm_blur = tf.clip_by_value(gpu_targetm_blur, 0, 0.5) * 2
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gpu_target_t_blur = gpu_target_t*gpu_targetm_blur
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gpu_pred_t_blur = gpu_pred_t*gpu_targetm_t
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# Structural loss
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gpu_loss = tf.reduce_mean (5*nn.dssim(gpu_target_t_blur, gpu_pred_t_blur, max_val=1.0, filter_size=int(resolution/11.6)), axis=[1])
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gpu_loss += tf.reduce_mean (5*nn.dssim(gpu_target_t_blur, gpu_pred_t_blur, max_val=1.0, filter_size=int(resolution/23.2)), axis=[1])
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# Pixel loss
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gpu_loss += tf.reduce_mean (10*tf.square(gpu_target_t_blur-gpu_pred_t_blur), axis=[1,2,3])
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else:
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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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@ -110,9 +136,14 @@ class XSegModel(ModelBase):
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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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return l
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if self.pretrain:
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def train(input_np, target_np, targetm_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, targetm_t :targetm_np })
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return l
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else:
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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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return l
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self.train = train
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def view(input_np):
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@ -124,30 +155,40 @@ 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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if self.pretrain:
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pretrain_gen = SampleGeneratorFace(self.get_pretraining_data_path(), debug=self.is_debug(), batch_size=self.get_batch_size(),
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sample_process_options=SampleProcessor.Options(random_flip=True),
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output_sample_types = [ {'sample_type': SampleProcessor.SampleType.FACE_IMAGE,'warp':True, 'transform':True, 'channel_type' : SampleProcessor.ChannelType.BGR, 'face_type':self.face_type, 'data_format':nn.data_format, 'resolution': resolution},
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{'sample_type': SampleProcessor.SampleType.FACE_IMAGE,'warp':True, 'transform':True, 'channel_type' : SampleProcessor.ChannelType.G, 'face_type':self.face_type, 'data_format':nn.data_format, 'resolution': resolution},
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{'sample_type': SampleProcessor.SampleType.FACE_MASK, 'warp':True, 'transform':True, 'channel_type' : SampleProcessor.ChannelType.G, 'face_mask_type' : SampleProcessor.FaceMaskType.FULL_FACE, 'face_type':self.face_type, 'data_format':nn.data_format, 'resolution': resolution},
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],
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uniform_yaw_distribution=False,
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generators_count=cpu_count )
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self.set_training_data_generators ([pretrain_gen])
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else:
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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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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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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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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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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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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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self.set_training_data_generators ([srcdst_generator, src_generator, dst_generator])
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#override
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def get_model_filename_list(self):
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@ -159,16 +200,25 @@ class XSegModel(ModelBase):
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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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if self.pretrain:
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image_np, target_np, targetm_np = self.generate_next_samples()[0]
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loss = self.train (image_np, target_np, targetm_np)
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else:
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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, for_history=False):
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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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image_np, mask_np = srcdst_samples
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if self.pretrain:
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srcdst_samples, = samples
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image_np, mask_np, _ = srcdst_samples
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else:
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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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M, IM, = [ np.repeat (x, (3,), -1) for x in [M, IM] ]
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@ -178,11 +228,14 @@ class XSegModel(ModelBase):
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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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if self.pretrain:
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ar = I[i], IM[i]
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else:
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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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if not self.pretrain and len(src_samples) != 0:
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src_np, = src_samples
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@ -196,7 +249,7 @@ class XSegModel(ModelBase):
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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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if not self.pretrain and len(dst_samples) != 0:
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dst_np, = dst_samples
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