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global refactoring and fixes,

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removed support of extracted(aligned) PNG faces. Use old builds to convert from PNG to JPG.

fanseg model file in facelib/ is renamed
This commit is contained in:
Colombo 2020-03-13 08:09:00 +04:00
commit 61472cdaf7
82 changed files with 3838 additions and 3812 deletions
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34
models/Model_FANSeg/Model.py
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@ -18,21 +18,17 @@ class FANSegModel(ModelBase):
device_config = nn.getCurrentDeviceConfig()
yn_str = {True:'y',False:'n'}
#default_resolution = 256
#default_face_type = self.options['face_type'] = self.load_or_def_option('face_type', 'f')
ask_override = self.ask_override()
if self.is_first_run() or ask_override:
self.ask_autobackup_hour()
self.ask_target_iter()
self.ask_batch_size(24)
#if self.is_first_run():
#resolution = io.input_int("Resolution", default_resolution, add_info="64-512")
#resolution = np.clip ( (resolution // 16) * 16, 64, 512)
#self.options['resolution'] = resolution
#self.options['face_type'] = io.input_str ("Face type", default_face_type, ['f']).lower()
default_lr_dropout = self.options['lr_dropout'] = self.load_or_def_option('lr_dropout', False)
if self.is_first_run() or ask_override:
self.options['lr_dropout'] = io.input_bool ("Use learning rate dropout", default_lr_dropout, help_message="When the face is trained enough, you can enable this option to get extra sharpness and reduce subpixel shake for less amount of iterations.")
#override
def on_initialize(self):
device_config = nn.getCurrentDeviceConfig()
@ -42,11 +38,7 @@ class FANSegModel(ModelBase):
device_config = nn.getCurrentDeviceConfig()
devices = device_config.devices
self.resolution = resolution = 256#self.options['resolution']
#self.face_type = {'h' : FaceType.HALF,
# 'mf' : FaceType.MID_FULL,
# 'f' : FaceType.FULL,
# 'wf' : FaceType.WHOLE_FACE}[ self.options['face_type'] ]
self.resolution = resolution = 256
self.face_type = FaceType.FULL
place_model_on_cpu = len(devices) == 0
@ -56,13 +48,13 @@ class FANSegModel(ModelBase):
mask_shape = nn.get4Dshape(resolution,resolution,1)
# Initializing model classes
self.model = TernausNet(f'{self.model_name}_FANSeg',
self.model = TernausNet(f'{self.model_name}_FANSeg_{FaceType.toString(self.face_type)}',
resolution,
FaceType.toString(self.face_type),
load_weights=not self.is_first_run(),
weights_file_root=self.get_model_root_path(),
training=True,
place_model_on_cpu=place_model_on_cpu)
place_model_on_cpu=place_model_on_cpu,
optimizer=nn.RMSprop(lr=0.0001, lr_dropout=0.3 if self.options['lr_dropout'] else 1.0,name='opt') )
if self.is_training:
# Adjust batch size for multiple GPU
@ -93,15 +85,15 @@ class FANSegModel(ModelBase):
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])
gpu_losses += [gpu_loss]
gpu_loss_gvs += [ nn.tf_gradients ( gpu_loss, self.model.net_weights ) ]
gpu_loss_gvs += [ nn.gradients ( gpu_loss, self.model.net_weights ) ]
# Average losses and gradients, and create optimizer update ops
with tf.device (models_opt_device):
pred = nn.tf_concat(gpu_pred_list, 0)
pred = nn.concat(gpu_pred_list, 0)
loss = tf.reduce_mean(gpu_losses)
loss_gv_op = self.model.opt.get_update_op (nn.tf_average_gv_list (gpu_loss_gvs))
loss_gv_op = self.model.opt.get_update_op (nn.average_gv_list (gpu_loss_gvs))
# Initializing training and view functions
@ -171,7 +163,7 @@ class FANSegModel(ModelBase):
result = []
st = []
for i in range(n_samples):
ar = S[i]*TM[i]+ green_bg*(1-TM[i]), SM[i], S[i]*SM[i] + green_bg*(1-SM[i])
ar = S[i]*TM[i] + 0.5*S[i]*(1-TM[i]) + 0.5*green_bg*(1-TM[i]), SM[i], S[i]*SM[i] + green_bg*(1-SM[i])
st.append ( np.concatenate ( ar, axis=1) )
result += [ ('FANSeg training faces', np.concatenate (st, axis=0 )), ]