795 lines
39 KiB
Python
795 lines
39 KiB
Python
# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
|
|
#
|
|
# NVIDIA CORPORATION and its licensors retain all intellectual property
|
|
# and proprietary rights in and to this software, related documentation
|
|
# and any modifications thereto. Any use, reproduction, disclosure or
|
|
# distribution of this software and related documentation without an express
|
|
# license agreement from NVIDIA CORPORATION is strictly prohibited.
|
|
|
|
"""Network architectures from the paper
|
|
"Analyzing and Improving the Image Quality of StyleGAN".
|
|
Matches the original implementation of configs E-F by Karras et al. at
|
|
https://github.com/NVlabs/stylegan2/blob/master/training/networks_stylegan2.py"""
|
|
|
|
import numpy as np
|
|
import torch
|
|
from torch_utils import misc
|
|
from torch_utils import persistence
|
|
from torch_utils.ops import conv2d_resample
|
|
from torch_utils.ops import upfirdn2d
|
|
from torch_utils.ops import bias_act
|
|
from torch_utils.ops import fma
|
|
|
|
#----------------------------------------------------------------------------
|
|
|
|
@misc.profiled_function
|
|
def normalize_2nd_moment(x, dim=1, eps=1e-8):
|
|
return x * (x.square().mean(dim=dim, keepdim=True) + eps).rsqrt()
|
|
|
|
#----------------------------------------------------------------------------
|
|
|
|
@misc.profiled_function
|
|
def modulated_conv2d(
|
|
x, # Input tensor of shape [batch_size, in_channels, in_height, in_width].
|
|
weight, # Weight tensor of shape [out_channels, in_channels, kernel_height, kernel_width].
|
|
styles, # Modulation coefficients of shape [batch_size, in_channels].
|
|
noise = None, # Optional noise tensor to add to the output activations.
|
|
up = 1, # Integer upsampling factor.
|
|
down = 1, # Integer downsampling factor.
|
|
padding = 0, # Padding with respect to the upsampled image.
|
|
resample_filter = None, # Low-pass filter to apply when resampling activations. Must be prepared beforehand by calling upfirdn2d.setup_filter().
|
|
demodulate = True, # Apply weight demodulation?
|
|
flip_weight = True, # False = convolution, True = correlation (matches torch.nn.functional.conv2d).
|
|
fused_modconv = True, # Perform modulation, convolution, and demodulation as a single fused operation?
|
|
):
|
|
batch_size = x.shape[0]
|
|
out_channels, in_channels, kh, kw = weight.shape
|
|
misc.assert_shape(weight, [out_channels, in_channels, kh, kw]) # [OIkk]
|
|
misc.assert_shape(x, [batch_size, in_channels, None, None]) # [NIHW]
|
|
misc.assert_shape(styles, [batch_size, in_channels]) # [NI]
|
|
|
|
# Pre-normalize inputs to avoid FP16 overflow.
|
|
if x.dtype == torch.float16 and demodulate:
|
|
weight = weight * (1 / np.sqrt(in_channels * kh * kw) / weight.norm(float('inf'), dim=[1,2,3], keepdim=True)) # max_Ikk
|
|
styles = styles / styles.norm(float('inf'), dim=1, keepdim=True) # max_I
|
|
|
|
# Calculate per-sample weights and demodulation coefficients.
|
|
w = None
|
|
dcoefs = None
|
|
if demodulate or fused_modconv:
|
|
w = weight.unsqueeze(0) # [NOIkk]
|
|
w = w * styles.reshape(batch_size, 1, -1, 1, 1) # [NOIkk]
|
|
if demodulate:
|
|
dcoefs = (w.square().sum(dim=[2,3,4]) + 1e-8).rsqrt() # [NO]
|
|
if demodulate and fused_modconv:
|
|
w = w * dcoefs.reshape(batch_size, -1, 1, 1, 1) # [NOIkk]
|
|
|
|
# Execute by scaling the activations before and after the convolution.
|
|
if not fused_modconv:
|
|
x = x * styles.to(x.dtype).reshape(batch_size, -1, 1, 1)
|
|
x = conv2d_resample.conv2d_resample(x=x, w=weight.to(x.dtype), f=resample_filter, up=up, down=down, padding=padding, flip_weight=flip_weight)
|
|
if demodulate and noise is not None:
|
|
x = fma.fma(x, dcoefs.to(x.dtype).reshape(batch_size, -1, 1, 1), noise.to(x.dtype))
|
|
elif demodulate:
|
|
x = x * dcoefs.to(x.dtype).reshape(batch_size, -1, 1, 1)
|
|
elif noise is not None:
|
|
x = x.add_(noise.to(x.dtype))
|
|
return x
|
|
|
|
# Execute as one fused op using grouped convolution.
|
|
with misc.suppress_tracer_warnings(): # this value will be treated as a constant
|
|
batch_size = int(batch_size)
|
|
misc.assert_shape(x, [batch_size, in_channels, None, None])
|
|
x = x.reshape(1, -1, *x.shape[2:])
|
|
w = w.reshape(-1, in_channels, kh, kw)
|
|
x = conv2d_resample.conv2d_resample(x=x, w=w.to(x.dtype), f=resample_filter, up=up, down=down, padding=padding, groups=batch_size, flip_weight=flip_weight)
|
|
x = x.reshape(batch_size, -1, *x.shape[2:])
|
|
if noise is not None:
|
|
x = x.add_(noise)
|
|
return x
|
|
|
|
#----------------------------------------------------------------------------
|
|
|
|
@persistence.persistent_class
|
|
class FullyConnectedLayer(torch.nn.Module):
|
|
def __init__(self,
|
|
in_features, # Number of input features.
|
|
out_features, # Number of output features.
|
|
bias = True, # Apply additive bias before the activation function?
|
|
activation = 'linear', # Activation function: 'relu', 'lrelu', etc.
|
|
lr_multiplier = 1, # Learning rate multiplier.
|
|
bias_init = 0, # Initial value for the additive bias.
|
|
):
|
|
super().__init__()
|
|
self.in_features = in_features
|
|
self.out_features = out_features
|
|
self.activation = activation
|
|
self.weight = torch.nn.Parameter(torch.randn([out_features, in_features]) / lr_multiplier)
|
|
self.bias = torch.nn.Parameter(torch.full([out_features], np.float32(bias_init))) if bias else None
|
|
self.weight_gain = lr_multiplier / np.sqrt(in_features)
|
|
self.bias_gain = lr_multiplier
|
|
|
|
def forward(self, x):
|
|
w = self.weight.to(x.dtype) * self.weight_gain
|
|
b = self.bias
|
|
if b is not None:
|
|
b = b.to(x.dtype)
|
|
if self.bias_gain != 1:
|
|
b = b * self.bias_gain
|
|
|
|
if self.activation == 'linear' and b is not None:
|
|
x = torch.addmm(b.unsqueeze(0), x, w.t())
|
|
else:
|
|
x = x.matmul(w.t())
|
|
x = bias_act.bias_act(x, b, act=self.activation)
|
|
return x
|
|
|
|
def extra_repr(self):
|
|
return f'in_features={self.in_features:d}, out_features={self.out_features:d}, activation={self.activation:s}'
|
|
|
|
#----------------------------------------------------------------------------
|
|
|
|
@persistence.persistent_class
|
|
class Conv2dLayer(torch.nn.Module):
|
|
def __init__(self,
|
|
in_channels, # Number of input channels.
|
|
out_channels, # Number of output channels.
|
|
kernel_size, # Width and height of the convolution kernel.
|
|
bias = True, # Apply additive bias before the activation function?
|
|
activation = 'linear', # Activation function: 'relu', 'lrelu', etc.
|
|
up = 1, # Integer upsampling factor.
|
|
down = 1, # Integer downsampling factor.
|
|
resample_filter = [1,3,3,1], # Low-pass filter to apply when resampling activations.
|
|
conv_clamp = None, # Clamp the output to +-X, None = disable clamping.
|
|
channels_last = False, # Expect the input to have memory_format=channels_last?
|
|
trainable = True, # Update the weights of this layer during training?
|
|
):
|
|
super().__init__()
|
|
self.in_channels = in_channels
|
|
self.out_channels = out_channels
|
|
self.activation = activation
|
|
self.up = up
|
|
self.down = down
|
|
self.conv_clamp = conv_clamp
|
|
self.register_buffer('resample_filter', upfirdn2d.setup_filter(resample_filter))
|
|
self.padding = kernel_size // 2
|
|
self.weight_gain = 1 / np.sqrt(in_channels * (kernel_size ** 2))
|
|
self.act_gain = bias_act.activation_funcs[activation].def_gain
|
|
|
|
memory_format = torch.channels_last if channels_last else torch.contiguous_format
|
|
weight = torch.randn([out_channels, in_channels, kernel_size, kernel_size]).to(memory_format=memory_format)
|
|
bias = torch.zeros([out_channels]) if bias else None
|
|
if trainable:
|
|
self.weight = torch.nn.Parameter(weight)
|
|
self.bias = torch.nn.Parameter(bias) if bias is not None else None
|
|
else:
|
|
self.register_buffer('weight', weight)
|
|
if bias is not None:
|
|
self.register_buffer('bias', bias)
|
|
else:
|
|
self.bias = None
|
|
|
|
def forward(self, x, gain=1):
|
|
w = self.weight * self.weight_gain
|
|
b = self.bias.to(x.dtype) if self.bias is not None else None
|
|
flip_weight = (self.up == 1) # slightly faster
|
|
x = conv2d_resample.conv2d_resample(x=x, w=w.to(x.dtype), f=self.resample_filter, up=self.up, down=self.down, padding=self.padding, flip_weight=flip_weight)
|
|
|
|
act_gain = self.act_gain * gain
|
|
act_clamp = self.conv_clamp * gain if self.conv_clamp is not None else None
|
|
x = bias_act.bias_act(x, b, act=self.activation, gain=act_gain, clamp=act_clamp)
|
|
return x
|
|
|
|
def extra_repr(self):
|
|
return ' '.join([
|
|
f'in_channels={self.in_channels:d}, out_channels={self.out_channels:d}, activation={self.activation:s},',
|
|
f'up={self.up}, down={self.down}'])
|
|
|
|
#----------------------------------------------------------------------------
|
|
|
|
@persistence.persistent_class
|
|
class MappingNetwork(torch.nn.Module):
|
|
def __init__(self,
|
|
z_dim, # Input latent (Z) dimensionality, 0 = no latent.
|
|
c_dim, # Conditioning label (C) dimensionality, 0 = no label.
|
|
w_dim, # Intermediate latent (W) dimensionality.
|
|
num_ws, # Number of intermediate latents to output, None = do not broadcast.
|
|
num_layers = 8, # Number of mapping layers.
|
|
embed_features = None, # Label embedding dimensionality, None = same as w_dim.
|
|
layer_features = None, # Number of intermediate features in the mapping layers, None = same as w_dim.
|
|
activation = 'lrelu', # Activation function: 'relu', 'lrelu', etc.
|
|
lr_multiplier = 0.01, # Learning rate multiplier for the mapping layers.
|
|
w_avg_beta = 0.998, # Decay for tracking the moving average of W during training, None = do not track.
|
|
):
|
|
super().__init__()
|
|
self.z_dim = z_dim
|
|
self.c_dim = c_dim
|
|
self.w_dim = w_dim
|
|
self.num_ws = num_ws
|
|
self.num_layers = num_layers
|
|
self.w_avg_beta = w_avg_beta
|
|
|
|
if embed_features is None:
|
|
embed_features = w_dim
|
|
if c_dim == 0:
|
|
embed_features = 0
|
|
if layer_features is None:
|
|
layer_features = w_dim
|
|
features_list = [z_dim + embed_features] + [layer_features] * (num_layers - 1) + [w_dim]
|
|
|
|
if c_dim > 0:
|
|
self.embed = FullyConnectedLayer(c_dim, embed_features)
|
|
for idx in range(num_layers):
|
|
in_features = features_list[idx]
|
|
out_features = features_list[idx + 1]
|
|
layer = FullyConnectedLayer(in_features, out_features, activation=activation, lr_multiplier=lr_multiplier)
|
|
setattr(self, f'fc{idx}', layer)
|
|
|
|
if num_ws is not None and w_avg_beta is not None:
|
|
self.register_buffer('w_avg', torch.zeros([w_dim]))
|
|
|
|
def forward(self, z, c, truncation_psi=1, truncation_cutoff=None, update_emas=False):
|
|
# Embed, normalize, and concat inputs.
|
|
x = None
|
|
with torch.autograd.profiler.record_function('input'):
|
|
if self.z_dim > 0:
|
|
misc.assert_shape(z, [None, self.z_dim])
|
|
x = normalize_2nd_moment(z.to(torch.float32))
|
|
if self.c_dim > 0:
|
|
misc.assert_shape(c, [None, self.c_dim])
|
|
y = normalize_2nd_moment(self.embed(c.to(torch.float32)))
|
|
x = torch.cat([x, y], dim=1) if x is not None else y
|
|
|
|
# Main layers.
|
|
for idx in range(self.num_layers):
|
|
layer = getattr(self, f'fc{idx}')
|
|
x = layer(x)
|
|
|
|
# Update moving average of W.
|
|
if update_emas and self.w_avg_beta is not None:
|
|
with torch.autograd.profiler.record_function('update_w_avg'):
|
|
self.w_avg.copy_(x.detach().mean(dim=0).lerp(self.w_avg, self.w_avg_beta))
|
|
|
|
# Broadcast.
|
|
if self.num_ws is not None:
|
|
with torch.autograd.profiler.record_function('broadcast'):
|
|
x = x.unsqueeze(1).repeat([1, self.num_ws, 1])
|
|
|
|
# Apply truncation.
|
|
if truncation_psi != 1:
|
|
with torch.autograd.profiler.record_function('truncate'):
|
|
assert self.w_avg_beta is not None
|
|
if self.num_ws is None or truncation_cutoff is None:
|
|
x = self.w_avg.lerp(x, truncation_psi)
|
|
else:
|
|
x[:, :truncation_cutoff] = self.w_avg.lerp(x[:, :truncation_cutoff], truncation_psi)
|
|
return x
|
|
|
|
def extra_repr(self):
|
|
return f'z_dim={self.z_dim:d}, c_dim={self.c_dim:d}, w_dim={self.w_dim:d}, num_ws={self.num_ws:d}'
|
|
|
|
#----------------------------------------------------------------------------
|
|
|
|
@persistence.persistent_class
|
|
class SynthesisLayer(torch.nn.Module):
|
|
def __init__(self,
|
|
in_channels, # Number of input channels.
|
|
out_channels, # Number of output channels.
|
|
w_dim, # Intermediate latent (W) dimensionality.
|
|
resolution, # Resolution of this layer.
|
|
kernel_size = 3, # Convolution kernel size.
|
|
up = 1, # Integer upsampling factor.
|
|
use_noise = True, # Enable noise input?
|
|
activation = 'lrelu', # Activation function: 'relu', 'lrelu', etc.
|
|
resample_filter = [1,3,3,1], # Low-pass filter to apply when resampling activations.
|
|
conv_clamp = None, # Clamp the output of convolution layers to +-X, None = disable clamping.
|
|
channels_last = False, # Use channels_last format for the weights?
|
|
):
|
|
super().__init__()
|
|
self.in_channels = in_channels
|
|
self.out_channels = out_channels
|
|
self.w_dim = w_dim
|
|
self.resolution = resolution
|
|
self.up = up
|
|
self.use_noise = use_noise
|
|
self.activation = activation
|
|
self.conv_clamp = conv_clamp
|
|
self.register_buffer('resample_filter', upfirdn2d.setup_filter(resample_filter))
|
|
self.padding = kernel_size // 2
|
|
self.act_gain = bias_act.activation_funcs[activation].def_gain
|
|
|
|
self.affine = FullyConnectedLayer(w_dim, in_channels, bias_init=1)
|
|
memory_format = torch.channels_last if channels_last else torch.contiguous_format
|
|
self.weight = torch.nn.Parameter(torch.randn([out_channels, in_channels, kernel_size, kernel_size]).to(memory_format=memory_format))
|
|
if use_noise:
|
|
self.register_buffer('noise_const', torch.randn([resolution, resolution]))
|
|
self.noise_strength = torch.nn.Parameter(torch.zeros([]))
|
|
self.bias = torch.nn.Parameter(torch.zeros([out_channels]))
|
|
|
|
def forward(self, x, w, noise_mode='random', fused_modconv=True, gain=1):
|
|
assert noise_mode in ['random', 'const', 'none']
|
|
in_resolution = self.resolution // self.up
|
|
misc.assert_shape(x, [None, self.in_channels, in_resolution, in_resolution])
|
|
styles = self.affine(w)
|
|
|
|
noise = None
|
|
if self.use_noise and noise_mode == 'random':
|
|
noise = torch.randn([x.shape[0], 1, self.resolution, self.resolution], device=x.device) * self.noise_strength
|
|
if self.use_noise and noise_mode == 'const':
|
|
noise = self.noise_const * self.noise_strength
|
|
|
|
flip_weight = (self.up == 1) # slightly faster
|
|
x = modulated_conv2d(x=x, weight=self.weight, styles=styles, noise=noise, up=self.up,
|
|
padding=self.padding, resample_filter=self.resample_filter, flip_weight=flip_weight, fused_modconv=fused_modconv)
|
|
|
|
act_gain = self.act_gain * gain
|
|
act_clamp = self.conv_clamp * gain if self.conv_clamp is not None else None
|
|
x = bias_act.bias_act(x, self.bias.to(x.dtype), act=self.activation, gain=act_gain, clamp=act_clamp)
|
|
return x
|
|
|
|
def extra_repr(self):
|
|
return ' '.join([
|
|
f'in_channels={self.in_channels:d}, out_channels={self.out_channels:d}, w_dim={self.w_dim:d},',
|
|
f'resolution={self.resolution:d}, up={self.up}, activation={self.activation:s}'])
|
|
|
|
#----------------------------------------------------------------------------
|
|
|
|
@persistence.persistent_class
|
|
class ToRGBLayer(torch.nn.Module):
|
|
def __init__(self, in_channels, out_channels, w_dim, kernel_size=1, conv_clamp=None, channels_last=False):
|
|
super().__init__()
|
|
self.in_channels = in_channels
|
|
self.out_channels = out_channels
|
|
self.w_dim = w_dim
|
|
self.conv_clamp = conv_clamp
|
|
self.affine = FullyConnectedLayer(w_dim, in_channels, bias_init=1)
|
|
memory_format = torch.channels_last if channels_last else torch.contiguous_format
|
|
self.weight = torch.nn.Parameter(torch.randn([out_channels, in_channels, kernel_size, kernel_size]).to(memory_format=memory_format))
|
|
self.bias = torch.nn.Parameter(torch.zeros([out_channels]))
|
|
self.weight_gain = 1 / np.sqrt(in_channels * (kernel_size ** 2))
|
|
|
|
def forward(self, x, w, fused_modconv=True):
|
|
styles = self.affine(w) * self.weight_gain
|
|
x = modulated_conv2d(x=x, weight=self.weight, styles=styles, demodulate=False, fused_modconv=fused_modconv)
|
|
x = bias_act.bias_act(x, self.bias.to(x.dtype), clamp=self.conv_clamp)
|
|
return x
|
|
|
|
def extra_repr(self):
|
|
return f'in_channels={self.in_channels:d}, out_channels={self.out_channels:d}, w_dim={self.w_dim:d}'
|
|
|
|
#----------------------------------------------------------------------------
|
|
|
|
@persistence.persistent_class
|
|
class SynthesisBlock(torch.nn.Module):
|
|
def __init__(self,
|
|
in_channels, # Number of input channels, 0 = first block.
|
|
out_channels, # Number of output channels.
|
|
w_dim, # Intermediate latent (W) dimensionality.
|
|
resolution, # Resolution of this block.
|
|
img_channels, # Number of output color channels.
|
|
is_last, # Is this the last block?
|
|
architecture = 'skip', # Architecture: 'orig', 'skip', 'resnet'.
|
|
resample_filter = [1,3,3,1], # Low-pass filter to apply when resampling activations.
|
|
conv_clamp = 256, # Clamp the output of convolution layers to +-X, None = disable clamping.
|
|
use_fp16 = False, # Use FP16 for this block?
|
|
fp16_channels_last = False, # Use channels-last memory format with FP16?
|
|
fused_modconv_default = True, # Default value of fused_modconv. 'inference_only' = True for inference, False for training.
|
|
**layer_kwargs, # Arguments for SynthesisLayer.
|
|
):
|
|
assert architecture in ['orig', 'skip', 'resnet']
|
|
super().__init__()
|
|
self.in_channels = in_channels
|
|
self.w_dim = w_dim
|
|
self.resolution = resolution
|
|
self.img_channels = img_channels
|
|
self.is_last = is_last
|
|
self.architecture = architecture
|
|
self.use_fp16 = use_fp16
|
|
self.channels_last = (use_fp16 and fp16_channels_last)
|
|
self.fused_modconv_default = fused_modconv_default
|
|
self.register_buffer('resample_filter', upfirdn2d.setup_filter(resample_filter))
|
|
self.num_conv = 0
|
|
self.num_torgb = 0
|
|
|
|
if in_channels == 0:
|
|
self.const = torch.nn.Parameter(torch.randn([out_channels, resolution, resolution]))
|
|
|
|
if in_channels != 0:
|
|
self.conv0 = SynthesisLayer(in_channels, out_channels, w_dim=w_dim, resolution=resolution, up=2,
|
|
resample_filter=resample_filter, conv_clamp=conv_clamp, channels_last=self.channels_last, **layer_kwargs)
|
|
self.num_conv += 1
|
|
|
|
self.conv1 = SynthesisLayer(out_channels, out_channels, w_dim=w_dim, resolution=resolution,
|
|
conv_clamp=conv_clamp, channels_last=self.channels_last, **layer_kwargs)
|
|
self.num_conv += 1
|
|
|
|
if is_last or architecture == 'skip':
|
|
self.torgb = ToRGBLayer(out_channels, img_channels, w_dim=w_dim,
|
|
conv_clamp=conv_clamp, channels_last=self.channels_last)
|
|
self.num_torgb += 1
|
|
|
|
if in_channels != 0 and architecture == 'resnet':
|
|
self.skip = Conv2dLayer(in_channels, out_channels, kernel_size=1, bias=False, up=2,
|
|
resample_filter=resample_filter, channels_last=self.channels_last)
|
|
|
|
def forward(self, x, img, ws, force_fp32=False, fused_modconv=None, update_emas=False, **layer_kwargs):
|
|
_ = update_emas # unused
|
|
misc.assert_shape(ws, [None, self.num_conv + self.num_torgb, self.w_dim])
|
|
w_iter = iter(ws.unbind(dim=1))
|
|
if ws.device.type != 'cuda':
|
|
force_fp32 = True
|
|
dtype = torch.float16 if self.use_fp16 and not force_fp32 else torch.float32
|
|
memory_format = torch.channels_last if self.channels_last and not force_fp32 else torch.contiguous_format
|
|
if fused_modconv is None:
|
|
fused_modconv = self.fused_modconv_default
|
|
if fused_modconv == 'inference_only':
|
|
fused_modconv = (not self.training)
|
|
|
|
# Input.
|
|
if self.in_channels == 0:
|
|
x = self.const.to(dtype=dtype, memory_format=memory_format)
|
|
x = x.unsqueeze(0).repeat([ws.shape[0], 1, 1, 1])
|
|
else:
|
|
misc.assert_shape(x, [None, self.in_channels, self.resolution // 2, self.resolution // 2])
|
|
x = x.to(dtype=dtype, memory_format=memory_format)
|
|
|
|
# Main layers.
|
|
if self.in_channels == 0:
|
|
x = self.conv1(x, next(w_iter), fused_modconv=fused_modconv, **layer_kwargs)
|
|
elif self.architecture == 'resnet':
|
|
y = self.skip(x, gain=np.sqrt(0.5))
|
|
x = self.conv0(x, next(w_iter), fused_modconv=fused_modconv, **layer_kwargs)
|
|
x = self.conv1(x, next(w_iter), fused_modconv=fused_modconv, gain=np.sqrt(0.5), **layer_kwargs)
|
|
x = y.add_(x)
|
|
else:
|
|
x = self.conv0(x, next(w_iter), fused_modconv=fused_modconv, **layer_kwargs)
|
|
x = self.conv1(x, next(w_iter), fused_modconv=fused_modconv, **layer_kwargs)
|
|
|
|
# ToRGB.
|
|
if img is not None:
|
|
misc.assert_shape(img, [None, self.img_channels, self.resolution // 2, self.resolution // 2])
|
|
img = upfirdn2d.upsample2d(img, self.resample_filter)
|
|
if self.is_last or self.architecture == 'skip':
|
|
y = self.torgb(x, next(w_iter), fused_modconv=fused_modconv)
|
|
y = y.to(dtype=torch.float32, memory_format=torch.contiguous_format)
|
|
img = img.add_(y) if img is not None else y
|
|
|
|
assert x.dtype == dtype
|
|
assert img is None or img.dtype == torch.float32
|
|
return x, img
|
|
|
|
def extra_repr(self):
|
|
return f'resolution={self.resolution:d}, architecture={self.architecture:s}'
|
|
|
|
#----------------------------------------------------------------------------
|
|
|
|
@persistence.persistent_class
|
|
class SynthesisNetwork(torch.nn.Module):
|
|
def __init__(self,
|
|
w_dim, # Intermediate latent (W) dimensionality.
|
|
img_resolution, # Output image resolution.
|
|
img_channels, # Number of color channels.
|
|
channel_base = 32768, # Overall multiplier for the number of channels.
|
|
channel_max = 512, # Maximum number of channels in any layer.
|
|
num_fp16_res = 4, # Use FP16 for the N highest resolutions.
|
|
**block_kwargs, # Arguments for SynthesisBlock.
|
|
):
|
|
assert img_resolution >= 4 and img_resolution & (img_resolution - 1) == 0
|
|
super().__init__()
|
|
self.w_dim = w_dim
|
|
self.img_resolution = img_resolution
|
|
self.img_resolution_log2 = int(np.log2(img_resolution))
|
|
self.img_channels = img_channels
|
|
self.num_fp16_res = num_fp16_res
|
|
self.block_resolutions = [2 ** i for i in range(2, self.img_resolution_log2 + 1)]
|
|
channels_dict = {res: min(channel_base // res, channel_max) for res in self.block_resolutions}
|
|
fp16_resolution = max(2 ** (self.img_resolution_log2 + 1 - num_fp16_res), 8)
|
|
|
|
self.num_ws = 0
|
|
for res in self.block_resolutions:
|
|
in_channels = channels_dict[res // 2] if res > 4 else 0
|
|
out_channels = channels_dict[res]
|
|
use_fp16 = (res >= fp16_resolution)
|
|
is_last = (res == self.img_resolution)
|
|
block = SynthesisBlock(in_channels, out_channels, w_dim=w_dim, resolution=res,
|
|
img_channels=img_channels, is_last=is_last, use_fp16=use_fp16, **block_kwargs)
|
|
self.num_ws += block.num_conv
|
|
if is_last:
|
|
self.num_ws += block.num_torgb
|
|
setattr(self, f'b{res}', block)
|
|
|
|
def forward(self, ws, **block_kwargs):
|
|
block_ws = []
|
|
with torch.autograd.profiler.record_function('split_ws'):
|
|
misc.assert_shape(ws, [None, self.num_ws, self.w_dim])
|
|
ws = ws.to(torch.float32)
|
|
w_idx = 0
|
|
for res in self.block_resolutions:
|
|
block = getattr(self, f'b{res}')
|
|
block_ws.append(ws.narrow(1, w_idx, block.num_conv + block.num_torgb))
|
|
w_idx += block.num_conv
|
|
|
|
x = img = None
|
|
for res, cur_ws in zip(self.block_resolutions, block_ws):
|
|
block = getattr(self, f'b{res}')
|
|
x, img = block(x, img, cur_ws, **block_kwargs)
|
|
return img
|
|
|
|
def extra_repr(self):
|
|
return ' '.join([
|
|
f'w_dim={self.w_dim:d}, num_ws={self.num_ws:d},',
|
|
f'img_resolution={self.img_resolution:d}, img_channels={self.img_channels:d},',
|
|
f'num_fp16_res={self.num_fp16_res:d}'])
|
|
|
|
#----------------------------------------------------------------------------
|
|
|
|
@persistence.persistent_class
|
|
class Generator(torch.nn.Module):
|
|
def __init__(self,
|
|
z_dim, # Input latent (Z) dimensionality.
|
|
c_dim, # Conditioning label (C) dimensionality.
|
|
w_dim, # Intermediate latent (W) dimensionality.
|
|
img_resolution, # Output resolution.
|
|
img_channels, # Number of output color channels.
|
|
mapping_kwargs = {}, # Arguments for MappingNetwork.
|
|
**synthesis_kwargs, # Arguments for SynthesisNetwork.
|
|
):
|
|
super().__init__()
|
|
self.z_dim = z_dim
|
|
self.c_dim = c_dim
|
|
self.w_dim = w_dim
|
|
self.img_resolution = img_resolution
|
|
self.img_channels = img_channels
|
|
self.synthesis = SynthesisNetwork(w_dim=w_dim, img_resolution=img_resolution, img_channels=img_channels, **synthesis_kwargs)
|
|
self.num_ws = self.synthesis.num_ws
|
|
self.mapping = MappingNetwork(z_dim=z_dim, c_dim=c_dim, w_dim=w_dim, num_ws=self.num_ws, **mapping_kwargs)
|
|
|
|
def forward(self, z, c, truncation_psi=1, truncation_cutoff=None, update_emas=False, **synthesis_kwargs):
|
|
ws = self.mapping(z, c, truncation_psi=truncation_psi, truncation_cutoff=truncation_cutoff, update_emas=update_emas)
|
|
img = self.synthesis(ws, update_emas=update_emas, **synthesis_kwargs)
|
|
return img
|
|
|
|
#----------------------------------------------------------------------------
|
|
|
|
@persistence.persistent_class
|
|
class DiscriminatorBlock(torch.nn.Module):
|
|
def __init__(self,
|
|
in_channels, # Number of input channels, 0 = first block.
|
|
tmp_channels, # Number of intermediate channels.
|
|
out_channels, # Number of output channels.
|
|
resolution, # Resolution of this block.
|
|
img_channels, # Number of input color channels.
|
|
first_layer_idx, # Index of the first layer.
|
|
architecture = 'resnet', # Architecture: 'orig', 'skip', 'resnet'.
|
|
activation = 'lrelu', # Activation function: 'relu', 'lrelu', etc.
|
|
resample_filter = [1,3,3,1], # Low-pass filter to apply when resampling activations.
|
|
conv_clamp = None, # Clamp the output of convolution layers to +-X, None = disable clamping.
|
|
use_fp16 = False, # Use FP16 for this block?
|
|
fp16_channels_last = False, # Use channels-last memory format with FP16?
|
|
freeze_layers = 0, # Freeze-D: Number of layers to freeze.
|
|
):
|
|
assert in_channels in [0, tmp_channels]
|
|
assert architecture in ['orig', 'skip', 'resnet']
|
|
super().__init__()
|
|
self.in_channels = in_channels
|
|
self.resolution = resolution
|
|
self.img_channels = img_channels
|
|
self.first_layer_idx = first_layer_idx
|
|
self.architecture = architecture
|
|
self.use_fp16 = use_fp16
|
|
self.channels_last = (use_fp16 and fp16_channels_last)
|
|
self.register_buffer('resample_filter', upfirdn2d.setup_filter(resample_filter))
|
|
|
|
self.num_layers = 0
|
|
def trainable_gen():
|
|
while True:
|
|
layer_idx = self.first_layer_idx + self.num_layers
|
|
trainable = (layer_idx >= freeze_layers)
|
|
self.num_layers += 1
|
|
yield trainable
|
|
trainable_iter = trainable_gen()
|
|
|
|
if in_channels == 0 or architecture == 'skip':
|
|
self.fromrgb = Conv2dLayer(img_channels, tmp_channels, kernel_size=1, activation=activation,
|
|
trainable=next(trainable_iter), conv_clamp=conv_clamp, channels_last=self.channels_last)
|
|
|
|
self.conv0 = Conv2dLayer(tmp_channels, tmp_channels, kernel_size=3, activation=activation,
|
|
trainable=next(trainable_iter), conv_clamp=conv_clamp, channels_last=self.channels_last)
|
|
|
|
self.conv1 = Conv2dLayer(tmp_channels, out_channels, kernel_size=3, activation=activation, down=2,
|
|
trainable=next(trainable_iter), resample_filter=resample_filter, conv_clamp=conv_clamp, channels_last=self.channels_last)
|
|
|
|
if architecture == 'resnet':
|
|
self.skip = Conv2dLayer(tmp_channels, out_channels, kernel_size=1, bias=False, down=2,
|
|
trainable=next(trainable_iter), resample_filter=resample_filter, channels_last=self.channels_last)
|
|
|
|
def forward(self, x, img, force_fp32=False):
|
|
if (x if x is not None else img).device.type != 'cuda':
|
|
force_fp32 = True
|
|
dtype = torch.float16 if self.use_fp16 and not force_fp32 else torch.float32
|
|
memory_format = torch.channels_last if self.channels_last and not force_fp32 else torch.contiguous_format
|
|
|
|
# Input.
|
|
if x is not None:
|
|
misc.assert_shape(x, [None, self.in_channels, self.resolution, self.resolution])
|
|
x = x.to(dtype=dtype, memory_format=memory_format)
|
|
|
|
# FromRGB.
|
|
if self.in_channels == 0 or self.architecture == 'skip':
|
|
misc.assert_shape(img, [None, self.img_channels, self.resolution, self.resolution])
|
|
img = img.to(dtype=dtype, memory_format=memory_format)
|
|
y = self.fromrgb(img)
|
|
x = x + y if x is not None else y
|
|
img = upfirdn2d.downsample2d(img, self.resample_filter) if self.architecture == 'skip' else None
|
|
|
|
# Main layers.
|
|
if self.architecture == 'resnet':
|
|
y = self.skip(x, gain=np.sqrt(0.5))
|
|
x = self.conv0(x)
|
|
x = self.conv1(x, gain=np.sqrt(0.5))
|
|
x = y.add_(x)
|
|
else:
|
|
x = self.conv0(x)
|
|
x = self.conv1(x)
|
|
|
|
assert x.dtype == dtype
|
|
return x, img
|
|
|
|
def extra_repr(self):
|
|
return f'resolution={self.resolution:d}, architecture={self.architecture:s}'
|
|
|
|
#----------------------------------------------------------------------------
|
|
|
|
@persistence.persistent_class
|
|
class MinibatchStdLayer(torch.nn.Module):
|
|
def __init__(self, group_size, num_channels=1):
|
|
super().__init__()
|
|
self.group_size = group_size
|
|
self.num_channels = num_channels
|
|
|
|
def forward(self, x):
|
|
N, C, H, W = x.shape
|
|
with misc.suppress_tracer_warnings(): # as_tensor results are registered as constants
|
|
G = torch.min(torch.as_tensor(self.group_size), torch.as_tensor(N)) if self.group_size is not None else N
|
|
F = self.num_channels
|
|
c = C // F
|
|
|
|
y = x.reshape(G, -1, F, c, H, W) # [GnFcHW] Split minibatch N into n groups of size G, and channels C into F groups of size c.
|
|
y = y - y.mean(dim=0) # [GnFcHW] Subtract mean over group.
|
|
y = y.square().mean(dim=0) # [nFcHW] Calc variance over group.
|
|
y = (y + 1e-8).sqrt() # [nFcHW] Calc stddev over group.
|
|
y = y.mean(dim=[2,3,4]) # [nF] Take average over channels and pixels.
|
|
y = y.reshape(-1, F, 1, 1) # [nF11] Add missing dimensions.
|
|
y = y.repeat(G, 1, H, W) # [NFHW] Replicate over group and pixels.
|
|
x = torch.cat([x, y], dim=1) # [NCHW] Append to input as new channels.
|
|
return x
|
|
|
|
def extra_repr(self):
|
|
return f'group_size={self.group_size}, num_channels={self.num_channels:d}'
|
|
|
|
#----------------------------------------------------------------------------
|
|
|
|
@persistence.persistent_class
|
|
class DiscriminatorEpilogue(torch.nn.Module):
|
|
def __init__(self,
|
|
in_channels, # Number of input channels.
|
|
cmap_dim, # Dimensionality of mapped conditioning label, 0 = no label.
|
|
resolution, # Resolution of this block.
|
|
img_channels, # Number of input color channels.
|
|
architecture = 'resnet', # Architecture: 'orig', 'skip', 'resnet'.
|
|
mbstd_group_size = 4, # Group size for the minibatch standard deviation layer, None = entire minibatch.
|
|
mbstd_num_channels = 1, # Number of features for the minibatch standard deviation layer, 0 = disable.
|
|
activation = 'lrelu', # Activation function: 'relu', 'lrelu', etc.
|
|
conv_clamp = None, # Clamp the output of convolution layers to +-X, None = disable clamping.
|
|
):
|
|
assert architecture in ['orig', 'skip', 'resnet']
|
|
super().__init__()
|
|
self.in_channels = in_channels
|
|
self.cmap_dim = cmap_dim
|
|
self.resolution = resolution
|
|
self.img_channels = img_channels
|
|
self.architecture = architecture
|
|
|
|
if architecture == 'skip':
|
|
self.fromrgb = Conv2dLayer(img_channels, in_channels, kernel_size=1, activation=activation)
|
|
self.mbstd = MinibatchStdLayer(group_size=mbstd_group_size, num_channels=mbstd_num_channels) if mbstd_num_channels > 0 else None
|
|
self.conv = Conv2dLayer(in_channels + mbstd_num_channels, in_channels, kernel_size=3, activation=activation, conv_clamp=conv_clamp)
|
|
self.fc = FullyConnectedLayer(in_channels * (resolution ** 2), in_channels, activation=activation)
|
|
self.out = FullyConnectedLayer(in_channels, 1 if cmap_dim == 0 else cmap_dim)
|
|
|
|
def forward(self, x, img, cmap, force_fp32=False):
|
|
misc.assert_shape(x, [None, self.in_channels, self.resolution, self.resolution]) # [NCHW]
|
|
_ = force_fp32 # unused
|
|
dtype = torch.float32
|
|
memory_format = torch.contiguous_format
|
|
|
|
# FromRGB.
|
|
x = x.to(dtype=dtype, memory_format=memory_format)
|
|
if self.architecture == 'skip':
|
|
misc.assert_shape(img, [None, self.img_channels, self.resolution, self.resolution])
|
|
img = img.to(dtype=dtype, memory_format=memory_format)
|
|
x = x + self.fromrgb(img)
|
|
|
|
# Main layers.
|
|
if self.mbstd is not None:
|
|
x = self.mbstd(x)
|
|
x = self.conv(x)
|
|
x = self.fc(x.flatten(1))
|
|
x = self.out(x)
|
|
|
|
# Conditioning.
|
|
if self.cmap_dim > 0:
|
|
misc.assert_shape(cmap, [None, self.cmap_dim])
|
|
x = (x * cmap).sum(dim=1, keepdim=True) * (1 / np.sqrt(self.cmap_dim))
|
|
|
|
assert x.dtype == dtype
|
|
return x
|
|
|
|
def extra_repr(self):
|
|
return f'resolution={self.resolution:d}, architecture={self.architecture:s}'
|
|
|
|
#----------------------------------------------------------------------------
|
|
|
|
@persistence.persistent_class
|
|
class Discriminator(torch.nn.Module):
|
|
def __init__(self,
|
|
c_dim, # Conditioning label (C) dimensionality.
|
|
img_resolution, # Input resolution.
|
|
img_channels, # Number of input color channels.
|
|
architecture = 'resnet', # Architecture: 'orig', 'skip', 'resnet'.
|
|
channel_base = 32768, # Overall multiplier for the number of channels.
|
|
channel_max = 512, # Maximum number of channels in any layer.
|
|
num_fp16_res = 4, # Use FP16 for the N highest resolutions.
|
|
conv_clamp = 256, # Clamp the output of convolution layers to +-X, None = disable clamping.
|
|
cmap_dim = None, # Dimensionality of mapped conditioning label, None = default.
|
|
block_kwargs = {}, # Arguments for DiscriminatorBlock.
|
|
mapping_kwargs = {}, # Arguments for MappingNetwork.
|
|
epilogue_kwargs = {}, # Arguments for DiscriminatorEpilogue.
|
|
):
|
|
super().__init__()
|
|
self.c_dim = c_dim
|
|
self.img_resolution = img_resolution
|
|
self.img_resolution_log2 = int(np.log2(img_resolution))
|
|
self.img_channels = img_channels
|
|
self.block_resolutions = [2 ** i for i in range(self.img_resolution_log2, 2, -1)]
|
|
channels_dict = {res: min(channel_base // res, channel_max) for res in self.block_resolutions + [4]}
|
|
fp16_resolution = max(2 ** (self.img_resolution_log2 + 1 - num_fp16_res), 8)
|
|
|
|
if cmap_dim is None:
|
|
cmap_dim = channels_dict[4]
|
|
if c_dim == 0:
|
|
cmap_dim = 0
|
|
|
|
common_kwargs = dict(img_channels=img_channels, architecture=architecture, conv_clamp=conv_clamp)
|
|
cur_layer_idx = 0
|
|
for res in self.block_resolutions:
|
|
in_channels = channels_dict[res] if res < img_resolution else 0
|
|
tmp_channels = channels_dict[res]
|
|
out_channels = channels_dict[res // 2]
|
|
use_fp16 = (res >= fp16_resolution)
|
|
block = DiscriminatorBlock(in_channels, tmp_channels, out_channels, resolution=res,
|
|
first_layer_idx=cur_layer_idx, use_fp16=use_fp16, **block_kwargs, **common_kwargs)
|
|
setattr(self, f'b{res}', block)
|
|
cur_layer_idx += block.num_layers
|
|
if c_dim > 0:
|
|
self.mapping = MappingNetwork(z_dim=0, c_dim=c_dim, w_dim=cmap_dim, num_ws=None, w_avg_beta=None, **mapping_kwargs)
|
|
self.b4 = DiscriminatorEpilogue(channels_dict[4], cmap_dim=cmap_dim, resolution=4, **epilogue_kwargs, **common_kwargs)
|
|
|
|
def forward(self, img, c, update_emas=False, **block_kwargs):
|
|
_ = update_emas # unused
|
|
x = None
|
|
for res in self.block_resolutions:
|
|
block = getattr(self, f'b{res}')
|
|
x, img = block(x, img, **block_kwargs)
|
|
|
|
cmap = None
|
|
if self.c_dim > 0:
|
|
cmap = self.mapping(None, c)
|
|
x = self.b4(x, img, cmap)
|
|
return x
|
|
|
|
def extra_repr(self):
|
|
return f'c_dim={self.c_dim:d}, img_resolution={self.img_resolution:d}, img_channels={self.img_channels:d}'
|
|
|
|
#----------------------------------------------------------------------------
|