mirror of
https://github.com/iperov/DeepFaceLab.git
synced 2025-08-14 02:37:00 -07:00
New script:
5.XSeg) data_dst/src mask for XSeg trainer - fetch.bat Copies faces containing XSeg polygons to aligned_xseg\ dir. Useful only if you want to collect labeled faces and reuse them in other fakes. Now you can use trained XSeg mask in the SAEHD training process. It’s mean default ‘full_face’ mask obtained from landmarks will be replaced with the mask obtained from the trained XSeg model. use 5.XSeg.optional) trained mask for data_dst/data_src - apply.bat 5.XSeg.optional) trained mask for data_dst/data_src - remove.bat Normally you don’t need it. You can use it, if you want to use ‘face_style’ and ‘bg_style’ with obstructions. XSeg trainer : now you can choose type of face XSeg trainer : now you can restart training in “override settings” Merger: XSeg-* modes now can be used with all types of faces. Therefore old MaskEditor, FANSEG models, and FAN-x modes have been removed, because the new XSeg solution is better, simpler and more convenient, which costs only 1 hour of manual masking for regular deepfake.
This commit is contained in:
Colombo
parent
e5bad483ca
commit
6d3607a13d
30 changed files with 279 additions and 1520 deletions
|
|
@ -1,188 +0,0 @@
|
|||
import multiprocessing
|
||||
import operator
|
||||
from functools import partial
|
||||
|
||||
import numpy as np
|
||||
|
||||
from core import mathlib
|
||||
from core.interact import interact as io
|
||||
from core.leras import nn
|
||||
from facelib import FaceType, TernausNet
|
||||
from models import ModelBase
|
||||
from samplelib import *
|
||||
|
||||
class FANSegModel(ModelBase):
|
||||
|
||||
def __init__(self, *args, **kwargs):
|
||||
super().__init__(*args, force_model_class_name='FANSeg', **kwargs)
|
||||
|
||||
#override
|
||||
def on_initialize_options(self):
|
||||
device_config = nn.getCurrentDeviceConfig()
|
||||
yn_str = {True:'y',False:'n'}
|
||||
|
||||
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)
|
||||
|
||||
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()
|
||||
nn.initialize(data_format="NHWC")
|
||||
tf = nn.tf
|
||||
|
||||
device_config = nn.getCurrentDeviceConfig()
|
||||
devices = device_config.devices
|
||||
|
||||
self.resolution = resolution = 256
|
||||
self.face_type = FaceType.FULL
|
||||
|
||||
place_model_on_cpu = len(devices) == 0
|
||||
models_opt_device = '/CPU:0' if place_model_on_cpu else '/GPU:0'
|
||||
|
||||
bgr_shape = nn.get4Dshape(resolution,resolution,3)
|
||||
mask_shape = nn.get4Dshape(resolution,resolution,1)
|
||||
|
||||
# Initializing model classes
|
||||
self.model = TernausNet(f'FANSeg_{FaceType.toString(self.face_type)}',
|
||||
resolution,
|
||||
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,
|
||||
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
|
||||
gpu_count = max(1, len(devices) )
|
||||
bs_per_gpu = max(1, self.get_batch_size() // gpu_count)
|
||||
self.set_batch_size( gpu_count*bs_per_gpu)
|
||||
|
||||
|
||||
# Compute losses per GPU
|
||||
gpu_pred_list = []
|
||||
|
||||
gpu_losses = []
|
||||
gpu_loss_gvs = []
|
||||
|
||||
for gpu_id in range(gpu_count):
|
||||
with tf.device( f'/GPU:{gpu_id}' if len(devices) != 0 else f'/CPU:0' ):
|
||||
|
||||
with tf.device(f'/CPU:0'):
|
||||
# slice on CPU, otherwise all batch data will be transfered to GPU first
|
||||
batch_slice = slice( gpu_id*bs_per_gpu, (gpu_id+1)*bs_per_gpu )
|
||||
gpu_input_t = self.model.input_t [batch_slice,:,:,:]
|
||||
gpu_target_t = self.model.target_t [batch_slice,:,:,:]
|
||||
|
||||
# process model tensors
|
||||
gpu_pred_logits_t, gpu_pred_t = self.model.net([gpu_input_t])
|
||||
gpu_pred_list.append(gpu_pred_t)
|
||||
|
||||
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.gradients ( gpu_loss, self.model.net_weights ) ]
|
||||
|
||||
|
||||
# Average losses and gradients, and create optimizer update ops
|
||||
with tf.device (models_opt_device):
|
||||
pred = nn.concat(gpu_pred_list, 0)
|
||||
loss = tf.reduce_mean(gpu_losses)
|
||||
|
||||
loss_gv_op = self.model.opt.get_update_op (nn.average_gv_list (gpu_loss_gvs))
|
||||
|
||||
|
||||
# Initializing training and view functions
|
||||
def train(input_np, target_np):
|
||||
l, _ = nn.tf_sess.run ( [loss, loss_gv_op], feed_dict={self.model.input_t :input_np, self.model.target_t :target_np })
|
||||
return l
|
||||
self.train = train
|
||||
|
||||
def view(input_np):
|
||||
return nn.tf_sess.run ( [pred], feed_dict={self.model.input_t :input_np})
|
||||
self.view = view
|
||||
|
||||
# initializing sample generators
|
||||
training_data_src_path = self.training_data_src_path
|
||||
training_data_dst_path = self.training_data_dst_path
|
||||
|
||||
cpu_count = min(multiprocessing.cpu_count(), 8)
|
||||
src_generators_count = cpu_count // 2
|
||||
dst_generators_count = cpu_count // 2
|
||||
src_generators_count = int(src_generators_count * 1.5)
|
||||
|
||||
src_generator = SampleGeneratorFace(training_data_src_path, random_ct_samples_path=training_data_src_path, debug=self.is_debug(), batch_size=self.get_batch_size(),
|
||||
sample_process_options=SampleProcessor.Options(random_flip=True),
|
||||
output_sample_types = [ {'sample_type': SampleProcessor.SampleType.FACE_IMAGE, 'ct_mode':'lct', 'warp':True, 'transform':True, 'channel_type' : SampleProcessor.ChannelType.BGR, 'face_type':self.face_type, 'random_motion_blur':(25, 5), 'random_gaussian_blur':(25,5), 'data_format':nn.data_format, 'resolution': resolution},
|
||||
{'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},
|
||||
],
|
||||
generators_count=src_generators_count )
|
||||
|
||||
dst_generator = SampleGeneratorFace(training_data_dst_path, debug=self.is_debug(), batch_size=self.get_batch_size(),
|
||||
sample_process_options=SampleProcessor.Options(random_flip=True),
|
||||
output_sample_types = [ {'sample_type': SampleProcessor.SampleType.FACE_IMAGE, 'warp':False, 'transform':True, 'channel_type' : SampleProcessor.ChannelType.BGR, 'face_type':self.face_type, 'data_format':nn.data_format, 'resolution': resolution},
|
||||
],
|
||||
generators_count=dst_generators_count,
|
||||
raise_on_no_data=False )
|
||||
if not dst_generator.is_initialized():
|
||||
io.log_info(f"\nTo view the model on unseen faces, place any aligned faces in {training_data_dst_path}.\n")
|
||||
|
||||
self.set_training_data_generators ([src_generator, dst_generator])
|
||||
|
||||
#override
|
||||
def get_model_filename_list(self):
|
||||
return self.model.model_filename_list
|
||||
|
||||
#override
|
||||
def onSave(self):
|
||||
self.model.save_weights()
|
||||
|
||||
#override
|
||||
def onTrainOneIter(self):
|
||||
source_np, target_np = self.generate_next_samples()[0]
|
||||
loss = self.train (source_np, target_np)
|
||||
|
||||
return ( ('loss', loss ), )
|
||||
|
||||
#override
|
||||
def onGetPreview(self, samples):
|
||||
n_samples = min(4, self.get_batch_size(), 800 // self.resolution )
|
||||
|
||||
src_samples, dst_samples = samples
|
||||
source_np, target_np = src_samples
|
||||
|
||||
S, TM, SM, = [ np.clip(x, 0.0, 1.0) for x in ([source_np,target_np] + self.view (source_np) ) ]
|
||||
TM, SM, = [ np.repeat (x, (3,), -1) for x in [TM, SM] ]
|
||||
|
||||
green_bg = np.tile( np.array([0,1,0], dtype=np.float32)[None,None,...], (self.resolution,self.resolution,1) )
|
||||
|
||||
result = []
|
||||
st = []
|
||||
for i in range(n_samples):
|
||||
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 )), ]
|
||||
|
||||
if len(dst_samples) != 0:
|
||||
dst_np, = dst_samples
|
||||
|
||||
D, DM, = [ np.clip(x, 0.0, 1.0) for x in ([dst_np] + self.view (dst_np) ) ]
|
||||
DM, = [ np.repeat (x, (3,), -1) for x in [DM] ]
|
||||
|
||||
st = []
|
||||
for i in range(n_samples):
|
||||
ar = D[i], DM[i], D[i]*DM[i]+ green_bg*(1-DM[i])
|
||||
st.append ( np.concatenate ( ar, axis=1) )
|
||||
|
||||
result += [ ('FANSeg unseen faces', np.concatenate (st, axis=0 )), ]
|
||||
|
||||
return result
|
||||
|
||||
Model = FANSegModel
|
||||
|
|
@ -1 +0,0 @@
|
|||
from .Model import Model
|
||||
|
|
@ -7,7 +7,7 @@ import numpy as np
|
|||
from core import mathlib
|
||||
from core.interact import interact as io
|
||||
from core.leras import nn
|
||||
from facelib import FaceType, TernausNet, XSegNet
|
||||
from facelib import FaceType, XSegNet
|
||||
from models import ModelBase
|
||||
from samplelib import *
|
||||
|
||||
|
|
@ -20,6 +20,19 @@ class XSegModel(ModelBase):
|
|||
def on_initialize_options(self):
|
||||
self.set_batch_size(4)
|
||||
|
||||
ask_override = self.ask_override()
|
||||
|
||||
default_face_type = self.options['face_type'] = self.load_or_def_option('face_type', 'wf')
|
||||
|
||||
if not self.is_first_run() and ask_override:
|
||||
self.restart_training = io.input_bool(f"Restart training?", False, help_message="Reset model weights and start training from scratch.")
|
||||
else:
|
||||
self.restart_training = False
|
||||
|
||||
if self.is_first_run():
|
||||
self.options['face_type'] = io.input_str ("Face type", default_face_type, ['h','mf','f','wf'], help_message="Half / mid face / full face / whole face. Choose the same as your deepfake model.").lower()
|
||||
|
||||
|
||||
#override
|
||||
def on_initialize(self):
|
||||
device_config = nn.getCurrentDeviceConfig()
|
||||
|
|
@ -31,7 +44,14 @@ class XSegModel(ModelBase):
|
|||
devices = device_config.devices
|
||||
|
||||
self.resolution = resolution = 256
|
||||
self.face_type = FaceType.WHOLE_FACE
|
||||
|
||||
if self.restart_training:
|
||||
self.set_iter(0)
|
||||
|
||||
self.face_type = {'h' : FaceType.HALF,
|
||||
'mf' : FaceType.MID_FULL,
|
||||
'f' : FaceType.FULL,
|
||||
'wf' : FaceType.WHOLE_FACE}[ self.options['face_type'] ]
|
||||
|
||||
place_model_on_cpu = len(devices) == 0
|
||||
models_opt_device = '/CPU:0' if place_model_on_cpu else '/GPU:0'
|
||||
|
|
@ -40,7 +60,7 @@ class XSegModel(ModelBase):
|
|||
mask_shape = nn.get4Dshape(resolution,resolution,1)
|
||||
|
||||
# Initializing model classes
|
||||
self.model = XSegNet(name=f'XSeg',
|
||||
self.model = XSegNet(name='XSeg',
|
||||
resolution=resolution,
|
||||
load_weights=not self.is_first_run(),
|
||||
weights_file_root=self.get_model_root_path(),
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue