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model_separate.py
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model_separate.py
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# Copyright 2019 Stanislav Pidhorskyi
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
import random
import losses
from modules import *
import numpy as np
class DLatent(nn.Module):
def __init__(self, dlatent_size, layer_count):
super(DLatent, self).__init__()
buffer = torch.zeros(layer_count, dlatent_size, dtype=torch.float32)
self.register_buffer('buff', buffer)
class Model(nn.Module):
def __init__(self, startf=32, maxf=256, layer_count=3, latent_size=128, mapping_layers=5, dlatent_avg_beta=None,
truncation_psi=None, truncation_cutoff=None, style_mixing_prob=None, channels=3, generator="", encoder=""):
super(Model, self).__init__()
self.layer_count = layer_count
self.mapping_fl = Mapping(
num_layers=2 * layer_count,
latent_size=latent_size,
dlatent_size=latent_size,
mapping_fmaps=latent_size,
mapping_layers=mapping_layers)
#self.decoder = Generator(
self.decoder = GENERATORS[generator](
startf=startf,
layer_count=layer_count,
maxf=maxf,
latent_size=latent_size,
channels=channels)
#self.encoder = Encoder_old(
self.encoder = ENCODERS[encoder](
startf=startf,
layer_count=layer_count,
maxf=maxf,
latent_size=latent_size,
channels=channels)
self.discriminator = Discriminator(
startf=startf,
layer_count=layer_count,
maxf=maxf,
channels=channels)
self.dlatent_avg = DLatent(latent_size, self.mapping_fl.num_layers)
self.latent_size = latent_size
self.dlatent_avg_beta = dlatent_avg_beta
self.truncation_psi = truncation_psi
self.style_mixing_prob = style_mixing_prob
self.truncation_cutoff = truncation_cutoff
def generate(self, lod, blend_factor, z=None, count=32, mixing=True, noise=True, return_styles=False, no_truncation=False):
if z is None:
z = torch.randn(count, self.latent_size)
styles = self.mapping_fl(z)[:, 0]
s = styles.view(styles.shape[0], 1, styles.shape[1])
styles = s.repeat(1, self.mapping_fl.num_layers, 1)
if self.dlatent_avg_beta is not None:
with torch.no_grad():
batch_avg = styles.mean(dim=0)
self.dlatent_avg.buff.data.lerp_(batch_avg.data, 1.0 - self.dlatent_avg_beta)
if mixing and self.style_mixing_prob is not None:
if random.random() < self.style_mixing_prob:
z2 = torch.randn(count, self.latent_size)
styles2 = self.mapping_fl(z2)[:, 0]
styles2 = styles2.view(styles2.shape[0], 1, styles2.shape[1]).repeat(1, self.mapping_fl.num_layers, 1)
layer_idx = torch.arange(self.mapping_fl.num_layers)[np.newaxis, :, np.newaxis]
cur_layers = (lod + 1) * 2
mixing_cutoff = random.randint(1, cur_layers)
styles = torch.where(layer_idx < mixing_cutoff, styles, styles2)
if (self.truncation_psi is not None) and not no_truncation:
layer_idx = torch.arange(self.mapping_fl.num_layers)[np.newaxis, :, np.newaxis]
ones = torch.ones(layer_idx.shape, dtype=torch.float32)
coefs = torch.where(layer_idx < self.truncation_cutoff, self.truncation_psi * ones, ones)
styles = torch.lerp(self.dlatent_avg.buff.data, styles, coefs)
rec = self.decoder.forward(styles, lod, blend_factor, noise)
if return_styles:
return s, rec
else:
return rec
def encode(self, x, lod, blend_factor):
Z = self.encoder(x, lod, blend_factor)
return Z
def forward(self, x, lod, blend_factor, d_train, ae, alt):
if ae:
self.encoder.requires_grad_(True)
z = torch.randn(x.shape[0], self.latent_size)
s, rec = self.generate(lod, blend_factor, z=z, mixing=False, noise=True, return_styles=True)
Z = self.encode(rec, lod, blend_factor)
assert Z.shape == s.shape
Lae = torch.mean(((Z - s.detach())**2))
return Lae
elif d_train:
with torch.no_grad():
Xp = self.generate(lod, blend_factor, count=x.shape[0], noise=True)
self.discriminator.requires_grad_(True)
d_result_real = self.discriminator(x, lod, blend_factor)
d_result_fake = self.discriminator(Xp.detach(), lod, blend_factor)
loss_d = losses.discriminator_logistic_simple_gp(d_result_fake, d_result_real, x)
return loss_d
else:
with torch.no_grad():
z = torch.randn(x.shape[0], self.latent_size)
self.discriminator.requires_grad_(False)
rec = self.generate(lod, blend_factor, count=x.shape[0], z=z.detach(), noise=True)
d_result_fake = self.discriminator(rec, lod, blend_factor)
loss_g = losses.generator_logistic_non_saturating(d_result_fake)
return loss_g
def lerp(self, other, betta):
if hasattr(other, 'module'):
other = other.module
with torch.no_grad():
params = list(self.mapping_fl.parameters()) + list(self.decoder.parameters()) + list(self.encoder.parameters()) + list(self.discriminator.parameters()) + list(self.dlatent_avg.parameters())
other_param = list(other.mapping_fl.parameters()) + list(other.decoder.parameters()) + list(other.encoder.parameters()) + list(other.dlatent_avg.parameters())
for p, p_other in zip(params, other_param):
p.data.lerp_(p_other.data, 1.0 - betta)