File size: 17,816 Bytes
fcd5579 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 |
import multiprocessing
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
from models import ModelBase
from samplelib import *
class QModel(ModelBase):
#override
def on_initialize(self):
device_config = nn.getCurrentDeviceConfig()
devices = device_config.devices
self.model_data_format = "NCHW" if len(devices) != 0 and not self.is_debug() else "NHWC"
nn.initialize(data_format=self.model_data_format)
tf = nn.tf
resolution = self.resolution = 96
self.face_type = FaceType.FULL
ae_dims = 128
e_dims = 64
d_dims = 64
d_mask_dims = 16
self.pretrain = False
self.pretrain_just_disabled = False
masked_training = True
models_opt_on_gpu = len(devices) >= 1 and all([dev.total_mem_gb >= 4 for dev in devices])
models_opt_device = nn.tf_default_device_name if models_opt_on_gpu and self.is_training else '/CPU:0'
optimizer_vars_on_cpu = models_opt_device=='/CPU:0'
input_ch = 3
bgr_shape = nn.get4Dshape(resolution,resolution,input_ch)
mask_shape = nn.get4Dshape(resolution,resolution,1)
self.model_filename_list = []
model_archi = nn.DeepFakeArchi(resolution, opts='ud')
with tf.device ('/CPU:0'):
#Place holders on CPU
self.warped_src = tf.placeholder (nn.floatx, bgr_shape)
self.warped_dst = tf.placeholder (nn.floatx, bgr_shape)
self.target_src = tf.placeholder (nn.floatx, bgr_shape)
self.target_dst = tf.placeholder (nn.floatx, bgr_shape)
self.target_srcm = tf.placeholder (nn.floatx, mask_shape)
self.target_dstm = tf.placeholder (nn.floatx, mask_shape)
# Initializing model classes
with tf.device (models_opt_device):
self.encoder = model_archi.Encoder(in_ch=input_ch, e_ch=e_dims, name='encoder')
encoder_out_ch = self.encoder.get_out_ch()*self.encoder.get_out_res(resolution)**2
self.inter = model_archi.Inter (in_ch=encoder_out_ch, ae_ch=ae_dims, ae_out_ch=ae_dims, name='inter')
inter_out_ch = self.inter.get_out_ch()
self.decoder_src = model_archi.Decoder(in_ch=inter_out_ch, d_ch=d_dims, d_mask_ch=d_mask_dims, name='decoder_src')
self.decoder_dst = model_archi.Decoder(in_ch=inter_out_ch, d_ch=d_dims, d_mask_ch=d_mask_dims, name='decoder_dst')
self.model_filename_list += [ [self.encoder, 'encoder.npy' ],
[self.inter, 'inter.npy' ],
[self.decoder_src, 'decoder_src.npy'],
[self.decoder_dst, 'decoder_dst.npy'] ]
if self.is_training:
self.src_dst_trainable_weights = self.encoder.get_weights() + self.inter.get_weights() + self.decoder_src.get_weights() + self.decoder_dst.get_weights()
# Initialize optimizers
self.src_dst_opt = nn.RMSprop(lr=2e-4, lr_dropout=0.3, name='src_dst_opt')
self.src_dst_opt.initialize_variables(self.src_dst_trainable_weights, vars_on_cpu=optimizer_vars_on_cpu )
self.model_filename_list += [ (self.src_dst_opt, 'src_dst_opt.npy') ]
if self.is_training:
# Adjust batch size for multiple GPU
gpu_count = max(1, len(devices) )
bs_per_gpu = max(1, 4 // gpu_count)
self.set_batch_size( gpu_count*bs_per_gpu)
# Compute losses per GPU
gpu_pred_src_src_list = []
gpu_pred_dst_dst_list = []
gpu_pred_src_dst_list = []
gpu_pred_src_srcm_list = []
gpu_pred_dst_dstm_list = []
gpu_pred_src_dstm_list = []
gpu_src_losses = []
gpu_dst_losses = []
gpu_src_dst_loss_gvs = []
for gpu_id in range(gpu_count):
with tf.device( f'/{devices[gpu_id].tf_dev_type}:{gpu_id}' if len(devices) != 0 else f'/CPU:0' ):
batch_slice = slice( gpu_id*bs_per_gpu, (gpu_id+1)*bs_per_gpu )
with tf.device(f'/CPU:0'):
# slice on CPU, otherwise all batch data will be transfered to GPU first
gpu_warped_src = self.warped_src [batch_slice,:,:,:]
gpu_warped_dst = self.warped_dst [batch_slice,:,:,:]
gpu_target_src = self.target_src [batch_slice,:,:,:]
gpu_target_dst = self.target_dst [batch_slice,:,:,:]
gpu_target_srcm = self.target_srcm[batch_slice,:,:,:]
gpu_target_dstm = self.target_dstm[batch_slice,:,:,:]
# process model tensors
gpu_src_code = self.inter(self.encoder(gpu_warped_src))
gpu_dst_code = self.inter(self.encoder(gpu_warped_dst))
gpu_pred_src_src, gpu_pred_src_srcm = self.decoder_src(gpu_src_code)
gpu_pred_dst_dst, gpu_pred_dst_dstm = self.decoder_dst(gpu_dst_code)
gpu_pred_src_dst, gpu_pred_src_dstm = self.decoder_src(gpu_dst_code)
gpu_pred_src_src_list.append(gpu_pred_src_src)
gpu_pred_dst_dst_list.append(gpu_pred_dst_dst)
gpu_pred_src_dst_list.append(gpu_pred_src_dst)
gpu_pred_src_srcm_list.append(gpu_pred_src_srcm)
gpu_pred_dst_dstm_list.append(gpu_pred_dst_dstm)
gpu_pred_src_dstm_list.append(gpu_pred_src_dstm)
gpu_target_srcm_blur = nn.gaussian_blur(gpu_target_srcm, max(1, resolution // 32) )
gpu_target_dstm_blur = nn.gaussian_blur(gpu_target_dstm, max(1, resolution // 32) )
gpu_target_dst_masked = gpu_target_dst*gpu_target_dstm_blur
gpu_target_dst_anti_masked = gpu_target_dst*(1.0 - gpu_target_dstm_blur)
gpu_target_src_masked_opt = gpu_target_src*gpu_target_srcm_blur if masked_training else gpu_target_src
gpu_target_dst_masked_opt = gpu_target_dst_masked if masked_training else gpu_target_dst
gpu_pred_src_src_masked_opt = gpu_pred_src_src*gpu_target_srcm_blur if masked_training else gpu_pred_src_src
gpu_pred_dst_dst_masked_opt = gpu_pred_dst_dst*gpu_target_dstm_blur if masked_training else gpu_pred_dst_dst
gpu_psd_target_dst_masked = gpu_pred_src_dst*gpu_target_dstm_blur
gpu_psd_target_dst_anti_masked = gpu_pred_src_dst*(1.0 - gpu_target_dstm_blur)
gpu_src_loss = tf.reduce_mean ( 10*nn.dssim(gpu_target_src_masked_opt, gpu_pred_src_src_masked_opt, max_val=1.0, filter_size=int(resolution/11.6)), axis=[1])
gpu_src_loss += tf.reduce_mean ( 10*tf.square ( gpu_target_src_masked_opt - gpu_pred_src_src_masked_opt ), axis=[1,2,3])
gpu_src_loss += tf.reduce_mean ( 10*tf.square( gpu_target_srcm - gpu_pred_src_srcm ),axis=[1,2,3] )
gpu_dst_loss = tf.reduce_mean ( 10*nn.dssim(gpu_target_dst_masked_opt, gpu_pred_dst_dst_masked_opt, max_val=1.0, filter_size=int(resolution/11.6) ), axis=[1])
gpu_dst_loss += tf.reduce_mean ( 10*tf.square( gpu_target_dst_masked_opt- gpu_pred_dst_dst_masked_opt ), axis=[1,2,3])
gpu_dst_loss += tf.reduce_mean ( 10*tf.square( gpu_target_dstm - gpu_pred_dst_dstm ),axis=[1,2,3] )
gpu_src_losses += [gpu_src_loss]
gpu_dst_losses += [gpu_dst_loss]
gpu_G_loss = gpu_src_loss + gpu_dst_loss
gpu_src_dst_loss_gvs += [ nn.gradients ( gpu_G_loss, self.src_dst_trainable_weights ) ]
# Average losses and gradients, and create optimizer update ops
with tf.device (models_opt_device):
pred_src_src = nn.concat(gpu_pred_src_src_list, 0)
pred_dst_dst = nn.concat(gpu_pred_dst_dst_list, 0)
pred_src_dst = nn.concat(gpu_pred_src_dst_list, 0)
pred_src_srcm = nn.concat(gpu_pred_src_srcm_list, 0)
pred_dst_dstm = nn.concat(gpu_pred_dst_dstm_list, 0)
pred_src_dstm = nn.concat(gpu_pred_src_dstm_list, 0)
src_loss = nn.average_tensor_list(gpu_src_losses)
dst_loss = nn.average_tensor_list(gpu_dst_losses)
src_dst_loss_gv = nn.average_gv_list (gpu_src_dst_loss_gvs)
src_dst_loss_gv_op = self.src_dst_opt.get_update_op (src_dst_loss_gv)
# Initializing training and view functions
def src_dst_train(warped_src, target_src, target_srcm, \
warped_dst, target_dst, target_dstm):
s, d, _ = nn.tf_sess.run ( [ src_loss, dst_loss, src_dst_loss_gv_op],
feed_dict={self.warped_src :warped_src,
self.target_src :target_src,
self.target_srcm:target_srcm,
self.warped_dst :warped_dst,
self.target_dst :target_dst,
self.target_dstm:target_dstm,
})
s = np.mean(s)
d = np.mean(d)
return s, d
self.src_dst_train = src_dst_train
def AE_view(warped_src, warped_dst):
return nn.tf_sess.run ( [pred_src_src, pred_dst_dst, pred_dst_dstm, pred_src_dst, pred_src_dstm],
feed_dict={self.warped_src:warped_src,
self.warped_dst:warped_dst})
self.AE_view = AE_view
else:
# Initializing merge function
with tf.device( nn.tf_default_device_name if len(devices) != 0 else f'/CPU:0'):
gpu_dst_code = self.inter(self.encoder(self.warped_dst))
gpu_pred_src_dst, gpu_pred_src_dstm = self.decoder_src(gpu_dst_code)
_, gpu_pred_dst_dstm = self.decoder_dst(gpu_dst_code)
def AE_merge( warped_dst):
return nn.tf_sess.run ( [gpu_pred_src_dst, gpu_pred_dst_dstm, gpu_pred_src_dstm], feed_dict={self.warped_dst:warped_dst})
self.AE_merge = AE_merge
# Loading/initializing all models/optimizers weights
for model, filename in io.progress_bar_generator(self.model_filename_list, "Initializing models"):
if self.pretrain_just_disabled:
do_init = False
if model == self.inter:
do_init = True
else:
do_init = self.is_first_run()
if not do_init:
do_init = not model.load_weights( self.get_strpath_storage_for_file(filename) )
if do_init and self.pretrained_model_path is not None:
pretrained_filepath = self.pretrained_model_path / filename
if pretrained_filepath.exists():
do_init = not model.load_weights(pretrained_filepath)
if do_init:
model.init_weights()
# initializing sample generators
if self.is_training:
training_data_src_path = self.training_data_src_path if not self.pretrain else self.get_pretraining_data_path()
training_data_dst_path = self.training_data_dst_path if not self.pretrain else self.get_pretraining_data_path()
cpu_count = min(multiprocessing.cpu_count(), 8)
src_generators_count = cpu_count // 2
dst_generators_count = cpu_count // 2
self.set_training_data_generators ([
SampleGeneratorFace(training_data_src_path, debug=self.is_debug(), batch_size=self.get_batch_size(),
sample_process_options=SampleProcessor.Options(random_flip=True if self.pretrain else False),
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},
{'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},
{'sample_type': SampleProcessor.SampleType.FACE_MASK, 'warp':False, '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 ),
SampleGeneratorFace(training_data_dst_path, debug=self.is_debug(), batch_size=self.get_batch_size(),
sample_process_options=SampleProcessor.Options(random_flip=True if self.pretrain else False),
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},
{'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},
{'sample_type': SampleProcessor.SampleType.FACE_MASK, 'warp':False, '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=dst_generators_count )
])
self.last_samples = None
#override
def get_model_filename_list(self):
return self.model_filename_list
#override
def onSave(self):
for model, filename in io.progress_bar_generator(self.get_model_filename_list(), "Saving", leave=False):
model.save_weights ( self.get_strpath_storage_for_file(filename) )
#override
def onTrainOneIter(self):
if self.get_iter() % 3 == 0 and self.last_samples is not None:
( (warped_src, target_src, target_srcm), \
(warped_dst, target_dst, target_dstm) ) = self.last_samples
warped_src = target_src
warped_dst = target_dst
else:
samples = self.last_samples = self.generate_next_samples()
( (warped_src, target_src, target_srcm), \
(warped_dst, target_dst, target_dstm) ) = samples
src_loss, dst_loss = self.src_dst_train (warped_src, target_src, target_srcm,
warped_dst, target_dst, target_dstm)
return ( ('src_loss', src_loss), ('dst_loss', dst_loss), )
#override
def onGetPreview(self, samples, for_history=False):
( (warped_src, target_src, target_srcm),
(warped_dst, target_dst, target_dstm) ) = samples
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) ) ]
DDM, SDM, = [ np.repeat (x, (3,), -1) for x in [DDM, SDM] ]
target_srcm, target_dstm = [ nn.to_data_format(x,"NHWC", self.model_data_format) for x in ([target_srcm, target_dstm] )]
n_samples = min(4, self.get_batch_size() )
result = []
st = []
for i in range(n_samples):
ar = S[i], SS[i], D[i], DD[i], SD[i]
st.append ( np.concatenate ( ar, axis=1) )
result += [ ('Quick96', np.concatenate (st, axis=0 )), ]
st_m = []
for i in range(n_samples):
ar = S[i]*target_srcm[i], SS[i], D[i]*target_dstm[i], DD[i]*DDM[i], SD[i]*(DDM[i]*SDM[i])
st_m.append ( np.concatenate ( ar, axis=1) )
result += [ ('Quick96 masked', np.concatenate (st_m, axis=0 )), ]
return result
def predictor_func (self, face=None):
face = nn.to_data_format(face[None,...], self.model_data_format, "NHWC")
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) ]
return bgr[0], mask_src_dstm[0][...,0], mask_dst_dstm[0][...,0]
#override
def get_MergerConfig(self):
import merger
return self.predictor_func, (self.resolution, self.resolution, 3), merger.MergerConfigMasked(face_type=self.face_type,
default_mode = 'overlay',
)
Model = QModel
|