github/DeepFaceLab
1
0
Fork 0
mirror of https://github.com/iperov/DeepFaceLab.git synced 2025-08-22 06:23:20 -07:00
Code Issues Projects Releases Packages Wiki Activity Actions

Fix gan options

Browse source
This commit is contained in:
jh 2021-03-22 12:00:44 -07:00
commit f3f2807972
1 changed files with 8 additions and 7 deletions
Download patch file Download diff file Expand all files Collapse all files

15
models/Model_SAEHD/Model.py
View file

@ -161,13 +161,14 @@ Examples: df, liae, df-d, df-ud, liae-ud, ...
else: else:
self.options['gan_power'] = np.clip ( io.input_number ("GAN power", default_gan_power, add_info="0.0 .. 1.0", help_message="Forces the neural network to learn small details of the face. Enable it only when the face is trained enough with lr_dropout(on) and random_warp(off), and don't disable. The higher the value, the higher the chances of artifacts. Typical fine value is 0.1"), 0.0, 1.0 ) self.options['gan_power'] = np.clip ( io.input_number ("GAN power", default_gan_power, add_info="0.0 .. 1.0", help_message="Forces the neural network to learn small details of the face. Enable it only when the face is trained enough with lr_dropout(on) and random_warp(off), and don't disable. The higher the value, the higher the chances of artifacts. Typical fine value is 0.1"), 0.0, 1.0 )
if self.options['gan_power'] != 0.0 and self.options['gan_version'] == 3: if self.options['gan_power'] != 0.0:
gan_patch_size = np.clip ( io.input_int("GAN patch size", default_gan_patch_size, add_info="3-640", help_message="The higher patch size, the higher the quality, the more VRAM is required. You can get sharper edges even at the lowest setting. Typical fine value is resolution / 8." ), 3, 640 ) if self.options['gan_version'] == 3:
self.options['gan_patch_size'] = gan_patch_size gan_patch_size = np.clip ( io.input_int("GAN patch size", default_gan_patch_size, add_info="3-640", help_message="The higher patch size, the higher the quality, the more VRAM is required. You can get sharper edges even at the lowest setting. Typical fine value is resolution / 8." ), 3, 640 )
self.options['gan_patch_size'] = gan_patch_size
gan_dims = np.clip ( io.input_int("GAN dimensions", default_gan_dims, add_info="4-64", help_message="The dimensions of the GAN network. The higher dimensions, the more VRAM is required. You can get sharper edges even at the lowest setting. Typical fine value is 16." ), 4, 64 )
self.options['gan_dims'] = gan_dims
gan_dims = np.clip ( io.input_int("GAN dimensions", default_gan_dims, add_info="4-64", help_message="The dimensions of the GAN network. The higher dimensions, the more VRAM is required. You can get sharper edges even at the lowest setting. Typical fine value is 16." ), 4, 64 )
self.options['gan_dims'] = gan_dims
self.options['gan_smoothing'] = np.clip ( io.input_number("GAN label smoothing", default_gan_smoothing, add_info="0 - 0.5", help_message="Uses soft labels with values slightly off from 0/1 for GAN, has a regularizing effect"), 0, 0.5) self.options['gan_smoothing'] = np.clip ( io.input_number("GAN label smoothing", default_gan_smoothing, add_info="0 - 0.5", help_message="Uses soft labels with values slightly off from 0/1 for GAN, has a regularizing effect"), 0, 0.5)
self.options['gan_noise'] = np.clip ( io.input_number("GAN noisy labels", default_gan_noise, add_info="0 - 0.5", help_message="Marks some images with the wrong label, helps prevent collapse"), 0, 0.5) self.options['gan_noise'] = np.clip ( io.input_number("GAN noisy labels", default_gan_noise, add_info="0 - 0.5", help_message="Marks some images with the wrong label, helps prevent collapse"), 0, 0.5)
@ -545,7 +546,7 @@ Examples: df, liae, df-d, df-ud, liae-ud, ...
num_labels = self.batch_size num_labels = self.batch_size
for d in tensor.get_shape().as_list()[1:]: for d in tensor.get_shape().as_list()[1:]:
num_labels *= d num_labels *= d
probs = tf.math.log([[noise, 1-noise]]) if label == 1 else tf.math.log([[1-noise, noise]]) probs = tf.math.log([[noise, 1-noise]]) if label == 1 else tf.math.log([[1-noise, noise]])
x = tf.random.categorical(probs, num_labels) x = tf.random.categorical(probs, num_labels)
x = tf.cast(x, tf.float32) x = tf.cast(x, tf.float32)