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ο»Ώ# Copyright (c) SenseTime Research. All rights reserved. | |
# Copyright (c) 2021, NVIDIA CORPORATION. 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. | |
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 | |
#---------------------------------------------------------------------------- | |
def normalize_2nd_moment(x, dim=1, eps=1e-8): | |
return x * (x.square().mean(dim=dim, keepdim=True) + eps).rsqrt() | |
#---------------------------------------------------------------------------- | |
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 | |
#---------------------------------------------------------------------------- | |
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.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 | |
#---------------------------------------------------------------------------- | |
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.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 | |
#---------------------------------------------------------------------------- | |
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 = 0.995, # 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, skip_w_avg_update=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 self.w_avg_beta is not None and self.training and not skip_w_avg_update: | |
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 | |
#---------------------------------------------------------------------------- | |
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? | |
square = False, # default if for rectangle images | |
): | |
super().__init__() | |
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.square=square | |
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: | |
if self.square: | |
self.register_buffer('noise_const', torch.randn([resolution, resolution])) | |
else: | |
self.register_buffer('noise_const', torch.randn([resolution, resolution // 2])) | |
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 | |
if self.square: | |
misc.assert_shape(x, [None, self.weight.shape[1], in_resolution, in_resolution]) | |
else: | |
misc.assert_shape(x, [None, self.weight.shape[1], in_resolution, in_resolution // 2]) | |
styles = self.affine(w) | |
noise = None | |
if self.use_noise and noise_mode == 'random': | |
if self.square: | |
noise = torch.randn([x.shape[0], 1, self.resolution, self.resolution], device=x.device) * self.noise_strength | |
else: | |
noise = torch.randn([x.shape[0], 1, self.resolution, self.resolution // 2], 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 | |
#---------------------------------------------------------------------------- | |
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.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 | |
#---------------------------------------------------------------------------- | |
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 = 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? | |
square = False, # default is for rectangle images | |
**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.register_buffer('resample_filter', upfirdn2d.setup_filter(resample_filter)) | |
self.num_conv = 0 | |
self.num_torgb = 0 | |
self.square = square | |
if in_channels == 0: | |
if self.square: | |
self.const = torch.nn.Parameter(torch.randn([out_channels, resolution, resolution])) | |
else: # rectangle | |
self.const = torch.nn.Parameter(torch.randn([out_channels, resolution, resolution // 2])) | |
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, square=square,**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, square=square, **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, **layer_kwargs): | |
misc.assert_shape(ws, [None, self.num_conv + self.num_torgb, self.w_dim]) | |
w_iter = iter(ws.unbind(dim=1)) | |
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: | |
with misc.suppress_tracer_warnings(): # this value will be treated as a constant | |
fused_modconv = (not self.training) and (dtype == torch.float32 or int(x.shape[0]) == 1) | |
# 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: | |
if self.square: | |
misc.assert_shape(x, [None, self.in_channels, self.resolution // 2, self.resolution // 2]) | |
else: # rectangle | |
misc.assert_shape(x, [None, self.in_channels, self.resolution // 2, self.resolution // 4]) | |
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: | |
if self.square: | |
misc.assert_shape(img, [None, self.img_channels, self.resolution // 2, self.resolution // 2]) | |
else: | |
misc.assert_shape(img, [None, self.img_channels, self.resolution // 2, self.resolution // 4]) | |
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 | |
#---------------------------------------------------------------------------- | |
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. | |
square, | |
channel_base = 32768, # Overall multiplier for the number of channels. | |
channel_max = 512, # Maximum number of channels in any layer. | |
num_fp16_res = 0, # 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.square=square | |
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,square=square, **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, return_feature=False, **block_kwargs): | |
block_ws = [] | |
features = [] | |
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) | |
features.append(x) | |
if return_feature: | |
return img, features | |
else: | |
return img | |
#---------------------------------------------------------------------------- | |
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. | |
square, | |
img_channels, # Number of output color channels. | |
mapping_kwargs = {}, # Arguments for MappingNetwork. | |
synthesis_kwargs = {}, # Arguments for SynthesisNetwork. | |
padding=False | |
): | |
super().__init__() | |
self.z_dim = z_dim | |
self.c_dim = c_dim | |
self.w_dim = w_dim | |
self.square = square | |
self.img_resolution = img_resolution | |
self.img_channels = img_channels | |
self.padding = padding | |
self.synthesis = SynthesisNetwork(w_dim=w_dim, img_resolution=img_resolution, img_channels=img_channels,square=square,**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, input_is_w=False, return_feature=False, **synthesis_kwargs): | |
if input_is_w: | |
ws = z | |
if ws.dim() == 2: | |
ws = ws.unsqueeze(1).repeat([1, self.mapping.num_ws, 1]) | |
else: | |
ws = self.mapping(z, c, truncation_psi=truncation_psi, truncation_cutoff=truncation_cutoff) | |
img = self.synthesis(ws, return_feature=return_feature, **synthesis_kwargs) | |
if return_feature: | |
img, feature = img | |
if self.padding: | |
pad = (img.size(2) - img.size(3)) // 2 | |
img = torch.nn.functional.pad(img, (pad, pad), "constant", 1) | |
if return_feature: | |
for i, feat in enumerate(feature): | |
pad = (feat.size(2) - feat.size(3)) // 2 | |
feature[i] = torch.nn.functional.pad(feat, (pad, pad), "constant", 0) | |
if return_feature: | |
return img, feature | |
else: | |
return img | |
#---------------------------------------------------------------------------- | |
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. | |
square = False, | |
): | |
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.square = square | |
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): | |
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: | |
if self.square: | |
misc.assert_shape(x, [None, self.in_channels, self.resolution, self.resolution]) | |
else: | |
misc.assert_shape(x, [None, self.in_channels, self.resolution, self.resolution // 2]) | |
x = x.to(dtype=dtype, memory_format=memory_format) | |
# FromRGB. | |
if self.in_channels == 0 or self.architecture == 'skip': | |
if self.square: | |
misc.assert_shape(img, [None, self.img_channels, self.resolution, self.resolution]) | |
else: | |
misc.assert_shape(img, [None, self.img_channels, self.resolution, self.resolution // 2]) | |
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 | |
#---------------------------------------------------------------------------- | |
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 | |
#---------------------------------------------------------------------------- | |
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. | |
square = False, | |
): | |
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 | |
self.square = square | |
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) | |
if self.square: | |
self.fc = FullyConnectedLayer(in_channels * (resolution ** 2), in_channels, activation=activation) | |
else: | |
self.fc = FullyConnectedLayer(in_channels * (resolution ** 2 // 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): | |
if self.square: | |
misc.assert_shape(x, [None, self.in_channels, self.resolution, self.resolution]) | |
else: | |
misc.assert_shape(x, [None, self.in_channels, self.resolution, self.resolution // 2]) # [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': | |
if self.square: | |
misc.assert_shape(img, [None, self.img_channels, self.resolution, self.resolution]) | |
else: | |
misc.assert_shape(img, [None, self.img_channels, self.resolution, self.resolution // 2]) | |
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 | |
#---------------------------------------------------------------------------- | |
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 = 0, # Use FP16 for the N highest resolutions. | |
conv_clamp = None, # Clamp the output of convolution layers to +-X, None = disable clamping. | |
cmap_dim = None, # Dimensionality of mapped conditioning label, None = default. | |
square = False, # default for rectangle images | |
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.square = square | |
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, square=square, **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, square=square, **epilogue_kwargs, **common_kwargs) | |
def forward(self, img, c, **block_kwargs): | |
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 | |
#---------------------------------------------------------------------------- | |