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SAEHD: removed option learn_mask, it is now enabled by default
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Colombo
parent
5eaa59cea6
commit
ba58621361
1 changed files with 41 additions and 64 deletions
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@ -37,7 +37,6 @@ class SAEHDModel(ModelBase):
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default_d_dims = self.options['d_dims'] = self.options.get('d_dims', None)
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default_d_mask_dims = self.options['d_mask_dims'] = self.options.get('d_mask_dims', None)
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default_masked_training = self.options['masked_training'] = self.load_or_def_option('masked_training', True)
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default_learn_mask = self.options['learn_mask'] = self.load_or_def_option('learn_mask', True)
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default_eyes_prio = self.options['eyes_prio'] = self.load_or_def_option('eyes_prio', False)
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default_lr_dropout = self.options['lr_dropout'] = self.load_or_def_option('lr_dropout', False)
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default_random_warp = self.options['random_warp'] = self.load_or_def_option('random_warp', True)
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@ -88,7 +87,6 @@ class SAEHDModel(ModelBase):
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if self.options['face_type'] == 'wf':
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self.options['masked_training'] = io.input_bool ("Masked training", default_masked_training, help_message="This option is available only for 'whole_face' type. Masked training clips training area to full_face mask, thus network will train the faces properly. When the face is trained enough, disable this option to train all area of the frame. Merge with 'raw-rgb' mode, then use Adobe After Effects to manually mask and compose whole face include forehead.")
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self.options['learn_mask'] = io.input_bool ("Learn mask", default_learn_mask, help_message="Learning mask will produce a smooth mask in the merger. Also it works as guide for neural network to recognize face directions.")
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self.options['eyes_prio'] = io.input_bool ("Eyes priority", default_eyes_prio, help_message='Helps to fix eye problems during training like "alien eyes" and wrong eyes direction ( especially on HD architectures ) by forcing the neural network to train eyes with higher priority. before/after https://i.imgur.com/YQHOuSR.jpg ')
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if self.is_first_run() or ask_override:
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@ -134,7 +132,6 @@ class SAEHDModel(ModelBase):
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'f' : FaceType.FULL,
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'wf' : FaceType.WHOLE_FACE}[ self.options['face_type'] ]
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learn_mask = self.options['learn_mask']
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eyes_prio = self.options['eyes_prio']
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archi = self.options['archi']
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is_hd = 'hd' in archi
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@ -234,14 +231,11 @@ class SAEHDModel(ModelBase):
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self.src_dst_opt = nn.TFRMSpropOptimizer(lr=lr, lr_dropout=lr_dropout, clipnorm=clipnorm, name='src_dst_opt')
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self.model_filename_list += [ (self.src_dst_opt, 'src_dst_opt.npy') ]
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if 'df' in archi:
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self.src_dst_all_trainable_weights = self.encoder.get_weights() + self.inter.get_weights() + self.decoder_src.get_weights() + self.decoder_dst.get_weights()
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self.src_dst_trainable_weights = self.encoder.get_weights() + self.inter.get_weights() + self.decoder_src.get_weights_ex(learn_mask) + self.decoder_dst.get_weights_ex(learn_mask)
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self.src_dst_trainable_weights = self.encoder.get_weights() + self.inter.get_weights() + self.decoder_src.get_weights() + self.decoder_dst.get_weights()
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elif 'liae' in archi:
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self.src_dst_all_trainable_weights = self.encoder.get_weights() + self.inter_AB.get_weights() + self.inter_B.get_weights() + self.decoder.get_weights()
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self.src_dst_trainable_weights = self.encoder.get_weights() + self.inter_AB.get_weights() + self.inter_B.get_weights() + self.decoder.get_weights_ex(learn_mask)
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self.src_dst_trainable_weights = self.encoder.get_weights() + self.inter_AB.get_weights() + self.inter_B.get_weights() + self.decoder.get_weights()
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self.src_dst_opt.initialize_variables (self.src_dst_all_trainable_weights, vars_on_cpu=optimizer_vars_on_cpu)
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self.src_dst_opt.initialize_variables (self.src_dst_trainable_weights, vars_on_cpu=optimizer_vars_on_cpu)
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if self.options['true_face_power'] != 0:
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self.D_code_opt = nn.TFRMSpropOptimizer(lr=lr, lr_dropout=lr_dropout, clipnorm=clipnorm, name='D_code_opt')
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@ -343,8 +337,7 @@ class SAEHDModel(ModelBase):
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if eyes_prio:
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gpu_src_loss += tf.reduce_mean ( 300*tf.abs ( gpu_target_src*gpu_target_srcm_eyes - gpu_pred_src_src*gpu_target_srcm_eyes ), axis=[1,2,3])
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if learn_mask:
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gpu_src_loss += tf.reduce_mean ( 10*tf.square( gpu_target_srcm - gpu_pred_src_srcm ),axis=[1,2,3] )
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gpu_src_loss += tf.reduce_mean ( 10*tf.square( gpu_target_srcm - gpu_pred_src_srcm ),axis=[1,2,3] )
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face_style_power = self.options['face_style_power'] / 100.0
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if face_style_power != 0 and not self.pretrain:
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@ -361,8 +354,7 @@ class SAEHDModel(ModelBase):
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if eyes_prio:
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gpu_dst_loss += tf.reduce_mean ( 300*tf.abs ( gpu_target_dst*gpu_target_dstm_eyes - gpu_pred_dst_dst*gpu_target_dstm_eyes ), axis=[1,2,3])
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if learn_mask:
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gpu_dst_loss += tf.reduce_mean ( 10*tf.square( gpu_target_dstm - gpu_pred_dst_dstm ),axis=[1,2,3] )
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gpu_dst_loss += tf.reduce_mean ( 10*tf.square( gpu_target_dstm - gpu_pred_dst_dstm ),axis=[1,2,3] )
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gpu_src_losses += [gpu_src_loss]
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gpu_dst_losses += [gpu_dst_loss]
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@ -461,16 +453,11 @@ class SAEHDModel(ModelBase):
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self.target_dstm_all:target_dstm_all})
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self.D_src_dst_train = D_src_dst_train
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if learn_mask:
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def AE_view(warped_src, warped_dst):
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return nn.tf_sess.run ( [pred_src_src, pred_dst_dst, pred_dst_dstm, pred_src_dst, pred_src_dstm],
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feed_dict={self.warped_src:warped_src,
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self.warped_dst:warped_dst})
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else:
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def AE_view(warped_src, warped_dst):
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return nn.tf_sess.run ( [pred_src_src, pred_dst_dst, pred_src_dst],
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feed_dict={self.warped_src:warped_src,
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self.warped_dst:warped_dst})
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def AE_view(warped_src, warped_dst):
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return nn.tf_sess.run ( [pred_src_src, pred_dst_dst, pred_dst_dstm, pred_src_dst, pred_src_dstm],
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feed_dict={self.warped_src:warped_src,
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self.warped_dst:warped_dst})
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self.AE_view = AE_view
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else:
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# Initializing merge function
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@ -490,12 +477,9 @@ class SAEHDModel(ModelBase):
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gpu_pred_src_dst, gpu_pred_src_dstm = self.decoder(gpu_src_dst_code)
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_, gpu_pred_dst_dstm = self.decoder(gpu_dst_code)
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if learn_mask:
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def AE_merge( warped_dst):
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return nn.tf_sess.run ( [gpu_pred_src_dst, gpu_pred_dst_dstm, gpu_pred_src_dstm], feed_dict={self.warped_dst:warped_dst})
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else:
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def AE_merge( warped_dst):
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return nn.tf_sess.run ( [gpu_pred_src_dst], feed_dict={self.warped_dst:warped_dst})
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def AE_merge( warped_dst):
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return nn.tf_sess.run ( [gpu_pred_src_dst, gpu_pred_dst_dstm, gpu_pred_src_dstm], feed_dict={self.warped_dst:warped_dst})
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self.AE_merge = AE_merge
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@ -605,11 +589,8 @@ class SAEHDModel(ModelBase):
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( (warped_src, target_src, target_srcm_all,),
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(warped_dst, target_dst, target_dstm_all,) ) = samples
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if self.options['learn_mask']:
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S, D, SS, DD, DDM, SD, SDM = [ np.clip( nn.to_data_format(x,"NHWC", self.model_data_format), 0.0, 1.0) for x in ([target_src,target_dst] + self.AE_view (target_src, target_dst) ) ]
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DDM, SDM, = [ np.repeat (x, (3,), -1) for x in [DDM, SDM] ]
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else:
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S, D, SS, DD, SD, = [ np.clip( nn.to_data_format(x,"NHWC", self.model_data_format) , 0.0, 1.0) for x in ([target_src,target_dst] + self.AE_view (target_src, target_dst) ) ]
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S, D, SS, DD, DDM, SD, SDM = [ np.clip( nn.to_data_format(x,"NHWC", self.model_data_format), 0.0, 1.0) for x in ([target_src,target_dst] + self.AE_view (target_src, target_dst) ) ]
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DDM, SDM, = [ np.repeat (x, (3,), -1) for x in [DDM, SDM] ]
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target_srcm_all, target_dstm_all = [ nn.to_data_format(x,"NHWC", self.model_data_format) for x in ([target_srcm_all, target_dstm_all] )]
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@ -627,13 +608,13 @@ class SAEHDModel(ModelBase):
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st.append ( np.concatenate ( ar, axis=1) )
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result += [ ('SAEHD', np.concatenate (st, axis=0 )), ]
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if self.options['learn_mask']:
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st_m = []
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for i in range(n_samples):
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ar = S[i]*target_srcm[i], SS[i], D[i]*target_dstm[i], DD[i]*DDM[i], SD[i]*(DDM[i]*SDM[i])
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st_m.append ( np.concatenate ( ar, axis=1) )
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st_m = []
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for i in range(n_samples):
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ar = S[i]*target_srcm[i], SS[i], D[i]*target_dstm[i], DD[i]*DDM[i], SD[i]*(DDM[i]*SDM[i])
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st_m.append ( np.concatenate ( ar, axis=1) )
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result += [ ('SAEHD masked', np.concatenate (st_m, axis=0 )), ]
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result += [ ('SAEHD masked', np.concatenate (st_m, axis=0 )), ]
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else:
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result = []
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@ -655,38 +636,34 @@ class SAEHDModel(ModelBase):
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st.append ( np.concatenate ( ar, axis=1) )
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result += [ ('SAEHD pred', np.concatenate (st, axis=0 )), ]
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if self.options['learn_mask']:
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st_m = []
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for i in range(n_samples):
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ar = S[i]*target_srcm[i], SS[i]
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st_m.append ( np.concatenate ( ar, axis=1) )
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result += [ ('SAEHD masked src-src', np.concatenate (st_m, axis=0 )), ]
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st_m = []
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for i in range(n_samples):
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ar = D[i]*target_dstm[i], DD[i]*DDM[i]
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st_m.append ( np.concatenate ( ar, axis=1) )
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result += [ ('SAEHD masked dst-dst', np.concatenate (st_m, axis=0 )), ]
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st_m = []
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for i in range(n_samples):
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ar = D[i]*target_dstm[i], SD[i]*(DDM[i]*SDM[i])
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st_m.append ( np.concatenate ( ar, axis=1) )
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result += [ ('SAEHD masked pred', np.concatenate (st_m, axis=0 )), ]
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st_m = []
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for i in range(n_samples):
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ar = S[i]*target_srcm[i], SS[i]
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st_m.append ( np.concatenate ( ar, axis=1) )
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result += [ ('SAEHD masked src-src', np.concatenate (st_m, axis=0 )), ]
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st_m = []
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for i in range(n_samples):
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ar = D[i]*target_dstm[i], DD[i]*DDM[i]
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st_m.append ( np.concatenate ( ar, axis=1) )
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result += [ ('SAEHD masked dst-dst', np.concatenate (st_m, axis=0 )), ]
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st_m = []
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for i in range(n_samples):
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ar = D[i]*target_dstm[i], SD[i]*(DDM[i]*SDM[i])
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st_m.append ( np.concatenate ( ar, axis=1) )
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result += [ ('SAEHD masked pred', np.concatenate (st_m, axis=0 )), ]
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return result
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def predictor_func (self, face=None):
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face = nn.to_data_format(face[None,...], self.model_data_format, "NHWC")
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if self.options['learn_mask']:
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bgr, mask_dst_dstm, mask_src_dstm = [ nn.to_data_format(x,"NHWC", self.model_data_format).astype(np.float32) for x in self.AE_merge (face) ]
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bgr, mask_dst_dstm, mask_src_dstm = [ nn.to_data_format(x,"NHWC", self.model_data_format).astype(np.float32) for x in self.AE_merge (face) ]
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mask = mask_dst_dstm[0] * mask_src_dstm[0]
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return bgr[0], mask[...,0]
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else:
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bgr, = [ nn.to_data_format(x,"NHWC", self.model_data_format).astype(np.float32) for x in self.AE_merge (face) ]
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return bgr[0]
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mask = mask_dst_dstm[0] * mask_src_dstm[0]
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return bgr[0], mask[...,0]
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#override
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def get_MergerConfig(self):
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