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DeepFaceLab/models/Model_Quick96/Model.py
iperov 5783191849 AMP: code refactoring, fix preview history 
added dumpdflive command
2021-06-26 10:44:41 +04:00

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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
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