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Fixed exportdfm and added missing code

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Cioscos 2021-10-06 13:03:57 +02:00
commit 70cb2bc7d4
5 changed files with 48 additions and 24 deletions
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1
core/leras/layers/__init__.py
View file

@ -16,3 +16,4 @@ from .ScaleAdd import *
from .DenseNorm import *
from .AdaIN import *
from .MsSsim import *
from .TanhPolar import *

59
core/leras/models/PatchDiscriminator.py
View file

@ -130,12 +130,14 @@ class UNetPatchDiscriminator(nn.ModelBase):
q=x[np.abs(np.array(x)-target_patch_size).argmin()]
return s[q][2]
def on_build(self, patch_size, in_ch, base_ch = 16):
def on_build(self, patch_size, in_ch, base_ch = 16, use_fp16 = False):
self.use_fp16 = use_fp16
conv_dtype = tf.float16 if use_fp16 else tf.float32
class ResidualBlock(nn.ModelBase):
def on_build(self, ch, kernel_size=3 ):
self.conv1 = nn.Conv2D( ch, ch, kernel_size=kernel_size, padding='SAME')
self.conv2 = nn.Conv2D( ch, ch, kernel_size=kernel_size, padding='SAME')
self.conv1 = nn.Conv2D( ch, ch, kernel_size=kernel_size, padding='SAME', dtype=conv_dtype)
self.conv2 = nn.Conv2D( ch, ch, kernel_size=kernel_size, padding='SAME', dtype=conv_dtype)
def forward(self, inp):
x = self.conv1(inp)
@ -155,26 +157,29 @@ class UNetPatchDiscriminator(nn.ModelBase):
level_chs = { i-1:v for i,v in enumerate([ min( base_ch * (2**i), 512 ) for i in range(len(layers)+1)]) }
self.in_conv = nn.Conv2D( in_ch, level_chs[-1], kernel_size=1, padding='VALID')
self.in_conv = nn.Conv2D( in_ch, level_chs[-1], kernel_size=1, padding='VALID', dtype=conv_dtype)
for i, (kernel_size, strides) in enumerate(layers):
self.convs.append ( nn.Conv2D( level_chs[i-1], level_chs[i], kernel_size=kernel_size, strides=strides, padding='SAME') )
self.convs.append ( nn.Conv2D( level_chs[i-1], level_chs[i], kernel_size=kernel_size, strides=strides, padding='SAME', dtype=conv_dtype) )
self.res1.append ( ResidualBlock(level_chs[i]) )
self.res2.append ( ResidualBlock(level_chs[i]) )
self.upconvs.insert (0, nn.Conv2DTranspose( level_chs[i]*(2 if i != len(layers)-1 else 1), level_chs[i-1], kernel_size=kernel_size, strides=strides, padding='SAME') )
self.upconvs.insert (0, nn.Conv2DTranspose( level_chs[i]*(2 if i != len(layers)-1 else 1), level_chs[i-1], kernel_size=kernel_size, strides=strides, padding='SAME', dtype=conv_dtype) )
self.upres1.insert (0, ResidualBlock(level_chs[i-1]*2) )
self.upres2.insert (0, ResidualBlock(level_chs[i-1]*2) )
self.out_conv = nn.Conv2D( level_chs[-1]*2, 1, kernel_size=1, padding='VALID')
self.out_conv = nn.Conv2D( level_chs[-1]*2, 1, kernel_size=1, padding='VALID', dtype=conv_dtype)
self.center_out = nn.Conv2D( level_chs[len(layers)-1], 1, kernel_size=1, padding='VALID')
self.center_conv = nn.Conv2D( level_chs[len(layers)-1], level_chs[len(layers)-1], kernel_size=1, padding='VALID')
def forward(self, x):
if self.use_fp16:
x = tf.cast(x, tf.float16)
x = tf.nn.leaky_relu( self.in_conv(x), 0.2 )
encs = []
@ -192,7 +197,13 @@ class UNetPatchDiscriminator(nn.ModelBase):
x = upres1(x)
x = upres2(x)
return center_out, self.out_conv(x)
x = self.out_conv(x)
if self.use_fp16:
center_out = tf.cast(center_out, tf.float32)
x = tf.cast(x, tf.float32)
return center_out, x
nn.UNetPatchDiscriminator = UNetPatchDiscriminator
@ -250,11 +261,14 @@ class UNetPatchDiscriminatorV2(nn.ModelBase):
q=x[np.abs(np.array(x)-target_patch_size).argmin()]
return s[q][2]
def on_build(self, patch_size, in_ch):
def on_build(self, patch_size, in_ch, use_fp16 = False):
self.use_fp16 = use_fp16
conv_dtype = tf.float16 if use_fp16 else tf.float32
class ResidualBlock(nn.ModelBase):
def on_build(self, ch, kernel_size=3 ):
self.conv1 = nn.Conv2D( ch, ch, kernel_size=kernel_size, padding='SAME')
self.conv2 = nn.Conv2D( ch, ch, kernel_size=kernel_size, padding='SAME')
self.conv1 = nn.Conv2D( ch, ch, kernel_size=kernel_size, padding='SAME', dtype=conv_dtype)
self.conv2 = nn.Conv2D( ch, ch, kernel_size=kernel_size, padding='SAME', dtype=conv_dtype)
def forward(self, inp):
x = self.conv1(inp)
@ -273,24 +287,27 @@ class UNetPatchDiscriminatorV2(nn.ModelBase):
level_chs = { i-1:v for i,v in enumerate([ min( base_ch * (2**i), 512 ) for i in range(len(layers)+1)]) }
self.in_conv = nn.Conv2D( in_ch, level_chs[-1], kernel_size=1, padding='VALID')
self.in_conv = nn.Conv2D( in_ch, level_chs[-1], kernel_size=1, padding='VALID', dtype=conv_dtype)
for i, (kernel_size, strides) in enumerate(layers):
self.convs.append ( nn.Conv2D( level_chs[i-1], level_chs[i], kernel_size=kernel_size, strides=strides, padding='SAME') )
self.convs.append ( nn.Conv2D( level_chs[i-1], level_chs[i], kernel_size=kernel_size, strides=strides, padding='SAME', dtype=conv_dtype) )
self.res.append ( ResidualBlock(level_chs[i]) )
self.upconvs.insert (0, nn.Conv2DTranspose( level_chs[i]*(2 if i != len(layers)-1 else 1), level_chs[i-1], kernel_size=kernel_size, strides=strides, padding='SAME') )
self.upconvs.insert (0, nn.Conv2DTranspose( level_chs[i]*(2 if i != len(layers)-1 else 1), level_chs[i-1], kernel_size=kernel_size, strides=strides, padding='SAME', dtype=conv_dtype) )
self.upres.insert (0, ResidualBlock(level_chs[i-1]*2) )
self.out_conv = nn.Conv2D( level_chs[-1]*2, 1, kernel_size=1, padding='VALID')
self.out_conv = nn.Conv2D( level_chs[-1]*2, 1, kernel_size=1, padding='VALID', dtype=conv_dtype)
self.center_out = nn.Conv2D( level_chs[len(layers)-1], 1, kernel_size=1, padding='VALID')
self.center_conv = nn.Conv2D( level_chs[len(layers)-1], level_chs[len(layers)-1], kernel_size=1, padding='VALID')
self.center_out = nn.Conv2D( level_chs[len(layers)-1], 1, kernel_size=1, padding='VALID', dtype=conv_dtype)
self.center_conv = nn.Conv2D( level_chs[len(layers)-1], level_chs[len(layers)-1], kernel_size=1, padding='VALID', dtype=conv_dtype)
def forward(self, x):
if self.use_fp16:
x = tf.cast(x, tf.float16)
x = tf.nn.leaky_relu( self.in_conv(x), 0.1 )
encs = []
@ -306,6 +323,12 @@ class UNetPatchDiscriminatorV2(nn.ModelBase):
x = tf.concat( [enc, x], axis=nn.conv2d_ch_axis)
x = upres(x)
return center_out, self.out_conv(x)
x = self.out_conv(x)
if self.use_fp16:
center_out = tf.cast(center_out, tf.float32)
x = tf.cast(x, tf.float32)
return center_out, x
nn.UNetPatchDiscriminatorV2 = UNetPatchDiscriminatorV2

2
core/leras/nn.py
View file

@ -107,7 +107,7 @@ class nn():
else:
nn.tf_default_device_name = f'/{device_config.devices[0].tf_dev_type}:0'
config = tf.ConfigProto(allow_soft_placement=True)
config = tf.ConfigProto()
config.gpu_options.visible_device_list = ','.join([str(device.index) for device in device_config.devices])
config.gpu_options.force_gpu_compatible = True

4
models/Model_AMP/Model.py
View file

@ -334,7 +334,7 @@ class AMPModel(ModelBase):
self.model_filename_list += [ (self.src_dst_opt, 'src_dst_opt.npy') ]
if gan_power != 0:
self.GAN = nn.UNetPatchDiscriminator(patch_size=self.options['gan_patch_size'], in_ch=input_ch, base_ch=self.options['gan_dims'], name="GAN")
self.GAN = nn.UNetPatchDiscriminator(patch_size=self.options['gan_patch_size'], in_ch=input_ch, base_ch=self.options['gan_dims'], use_fp16=use_fp16, name="GAN")
self.GAN_opt = nn.AdaBelief(lr=5e-5, lr_dropout=lr_dropout, clipnorm=clipnorm, name='GAN_opt')
self.GAN_opt.initialize_variables ( self.GAN.get_weights(), vars_on_cpu=optimizer_vars_on_cpu)
self.model_filename_list += [ [self.GAN, 'GAN.npy'],
@ -676,7 +676,7 @@ class AMPModel(ModelBase):
name='AMP',
input_names=['in_face:0','morph_value:0'],
output_names=['out_face_mask:0','out_celeb_face:0','out_celeb_face_mask:0'],
opset=9,
opset=11,
output_path=output_path)
#override

6
models/Model_SAEHD/Model.py
View file

@ -343,10 +343,10 @@ Examples: df, liae, df-d, df-ud, liae-ud, ...
if self.is_training:
if gan_power != 0:
if self.options['gan_version'] == 2:
self.D_src = nn.UNetPatchDiscriminatorV2(patch_size=resolution//16, in_ch=input_ch, name="D_src")
self.D_src = nn.UNetPatchDiscriminatorV2(patch_size=resolution//16, in_ch=input_ch, name="D_src", use_fp16=self.options['use_fp16'])
self.model_filename_list += [ [self.D_src, 'D_src_v2.npy'] ]
else:
self.D_src = nn.UNetPatchDiscriminator(patch_size=self.options['gan_patch_size'], in_ch=input_ch, base_ch=self.options['gan_dims'], name="D_src")
self.D_src = nn.UNetPatchDiscriminator(patch_size=self.options['gan_patch_size'], in_ch=input_ch, base_ch=self.options['gan_dims'], use_fp16=self.options['use_fp16'], name="D_src")
self.model_filename_list += [ [self.D_src, 'GAN.npy'] ]
# Initialize optimizers
@ -928,7 +928,7 @@ Examples: df, liae, df-d, df-ud, liae-ud, ...
target_src = np.stack( [ x[0] for x in src_samples_loss[:bs] ] )
target_srcm = np.stack( [ x[1] for x in src_samples_loss[:bs] ] )
target_srcm_em = np.stack( [ x[2] for x in src_samples_loss[:bs] ] )
target_srcm_em = np.stack( [ x[2] for x in src_samples_loss[:bs] ] )
target_dst = np.stack( [ x[0] for x in dst_samples_loss[:bs] ] )
target_dstm = np.stack( [ x[1] for x in dst_samples_loss[:bs] ] )