Comments (2)
Hi, thank you!
Yes, the current implementation uses conditioning only in DiscrEpilogue
: we decided to remove it from DiscriminatorBlock
, since it considerably increases the training time (~30% as far as I remember) and does not help that much (in terms of raw metrics at least). We also updated the paper accordingly a couple of weeks ago.
If you want to see our old "submission-time" version of `networks.py`, then here it is (sorry for the dirty code)
# 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
import torch.nn as nn
from torch import Tensor
from omegaconf import OmegaConf, DictConfig
import torch.nn.functional as F
from src.torch_utils import misc
from src.torch_utils import persistence
from src.torch_utils.ops import conv2d_resample, upfirdn2d, bias_act, fma
from training.motion import MotionEncoder
from training.layers import (
FullyConnectedLayer,
GenInput,
CoordFuser,
TimeFuser,
TemporalDifferenceEncoder,
MultiTimeEncoder,
JointTimeEncoder,
Conv2dLayer,
MappingNetwork,
remove_diag,
get_max_dist,
)
#----------------------------------------------------------------------------
@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) * 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
#----------------------------------------------------------------------------
@misc.profiled_function
def fmm_modulate_linear(x: Tensor, weight: Tensor, styles: Tensor, noise=None, activation: str="demod") -> Tensor:
"""
x: [batch_size, c_in, height, width]
weight: [c_out, c_in, 1, 1]
style: [batch_size, num_mod_params]
noise: Optional[batch_size, 1, height, width]
"""
batch_size, c_in, h, w = x.shape
c_out, c_in, kh, kw = weight.shape
rank = styles.shape[1] // (c_in + c_out)
assert kh == 1 and kw == 1
assert styles.shape[1] % (c_in + c_out) == 0
# Now, we need to construct a [c_out, c_in] matrix
left_matrix = styles[:, :c_out * rank] # [batch_size, left_matrix_size]
right_matrix = styles[:, c_out * rank:] # [batch_size, right_matrix_size]
left_matrix = left_matrix.view(batch_size, c_out, rank) # [batch_size, c_out, rank]
right_matrix = right_matrix.view(batch_size, rank, c_in) # [batch_size, rank, c_in]
# Imagine, that the output of `self.affine` (in SynthesisLayer) is N(0, 1)
# Then, std of weights is sqrt(rank). Converting it back to N(0, 1)
modulation = left_matrix @ right_matrix / np.sqrt(rank) # [batch_size, c_out, c_in]
if activation == "tanh":
modulation = modulation.tanh()
elif activation == "sigmoid":
modulation = modulation.sigmoid() - 0.5
W = weight.squeeze(3).squeeze(2).unsqueeze(0) * (modulation + 1.0) # [batch_size, c_out, c_in]
if activation == "demod":
W = W / (W.norm(dim=2, keepdim=True) + 1e-8) # [batch_size, c_out, c_in]
W = W.to(dtype=x.dtype)
# out = torch.einsum('boi,bihw->bohw', W, x)
x = x.view(batch_size, c_in, h * w) # [batch_size, c_in, h * w]
out = torch.bmm(W, x) # [batch_size, c_out, h * w]
out = out.view(batch_size, c_out, h, w) # [batch_size, c_out, h, w]
if not noise is None:
out = out.add_(noise)
return out
#----------------------------------------------------------------------------
@misc.profiled_function
def maybe_upsample(x, upsampling_mode: str, up: int) -> Tensor:
if up == 1:
return x
if upsampling_mode == 'bilinear':
x = F.interpolate(x, mode='bilinear', align_corners=True, scale_factor=up)
elif upsampling_mode == 'nearest':
x = F.interpolate(x, mode='nearest', scale_factor=up)
else:
raise NotImplementedError(f"Unknown upsampling mode: {upsampling_mode}")
return x
#----------------------------------------------------------------------------
@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.
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?
instance_norm = False, # Use instance norm?
cfg = {}, # Additional config
):
super().__init__()
self.cfg = cfg
self.resolution = resolution
self.use_fmm = self.resolution in self.cfg.fmm.get('resolutions', [])
self.up = up
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
if self.use_fmm:
self.affine = FullyConnectedLayer(w_dim, (in_channels + out_channels) * self.cfg.fmm.rank, bias_init=0)
else:
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 self.cfg.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]))
self.instance_norm = instance_norm
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.weight.shape[1], in_resolution, in_resolution])
styles = self.affine(w)
noise = None
if self.cfg.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.cfg.use_noise and noise_mode == 'const':
noise = self.noise_const * self.noise_strength
flip_weight = (self.up == 1) # slightly faster
if self.instance_norm:
x = x / (x.std(dim=[2,3], keepdim=True) + 1e-8) # [batch_size, c, h, w]
if self.use_fmm:
x = fmm_modulate_linear(x=x, weight=self.weight, styles=styles, noise=noise, activation=self.cfg.fmm.activation)
else:
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
#----------------------------------------------------------------------------
@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.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
#----------------------------------------------------------------------------
@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.
motion_w_dim, # Motion code size
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?
cfg = {}, # Additional config
**layer_kwargs, # Arguments for SynthesisLayer.
):
assert architecture in ['orig', 'skip', 'resnet']
super().__init__()
self.cfg = cfg
self.in_channels = in_channels
self.w_dim = w_dim
if resolution <= self.cfg.input.resolution:
self.resolution = self.cfg.input.resolution
self.up = 1
self.input_resolution = self.cfg.input.resolution
else:
self.resolution = resolution
self.up = 2
self.input_resolution = resolution // 2
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.use_fmm = self.resolution in self.cfg.fmm.get('resolutions', [])
self.kernel_size = 1 if self.use_fmm else 3
self.use_instance_norm = self.use_fmm and in_channels > 0 and cfg.get('fmm', {}).get('instance_norm', False)
if self.cfg.time_enc.per_resolution:
assert self.architecture != 'resnet'
self.time_fuser = TimeFuser(self.cfg, resolution=self.resolution, motion_w_dim=motion_w_dim)
self.time_emb_dim = self.time_fuser.get_total_dim()
else:
self.time_fuser = None
self.time_emb_dim = 0
if in_channels == 0:
self.input = GenInput(self.cfg, out_channels, w_dim, motion_w_dim=motion_w_dim)
conv1_in_channels = self.input.total_dim + self.time_emb_dim
else:
up_for_conv0 = 1 if self.use_fmm else self.up # For FMM, we'll upsample manually
if self.cfg.coords.enabled and (not self.cfg.coords.per_resolution or self.resolution > self.input_resolution):
assert self.architecture != 'resnet'
self.coord_fuser = CoordFuser(
cfg=self.cfg.coords,
w_dim=self.w_dim,
resolution=self.resolution // up_for_conv0,
t_resolution=self.cfg.max_num_frames)
conv0_in_channels = in_channels + self.coord_fuser.total_dim + self.time_emb_dim
else:
self.coord_fuser = None
conv0_in_channels = in_channels + self.time_emb_dim
# We are not using instance norm in conv0, because we concatenate coords to it (sometimes)
# and some coords can be all-zero
self.conv0 = SynthesisLayer(conv0_in_channels, out_channels, w_dim=w_dim, resolution=self.resolution, up=up_for_conv0,
resample_filter=resample_filter, conv_clamp=conv_clamp, channels_last=self.channels_last,
kernel_size=self.kernel_size, cfg=cfg, instance_norm=False, **layer_kwargs)
self.num_conv += 1
conv1_in_channels = out_channels
self.conv1 = SynthesisLayer(conv1_in_channels, out_channels, w_dim=w_dim, resolution=self.resolution,
conv_clamp=conv_clamp, channels_last=self.channels_last, kernel_size=self.kernel_size, cfg=cfg,
instance_norm=self.use_instance_norm, **layer_kwargs)
self.num_conv += 1
if self.cfg.get('num_extra_convs', {}).get(str(self.resolution), 0) > 0:
assert self.architecture != 'resnet', "Not implemented for resnet"
self.extra_convs = nn.ModuleList([
SynthesisLayer(out_channels, out_channels, w_dim=w_dim, resolution=self.resolution,
conv_clamp=conv_clamp, channels_last=self.channels_last, kernel_size=self.kernel_size,
instance_norm=self.use_instance_norm, cfg=cfg, **layer_kwargs)
for _ in range(self.cfg.num_extra_convs[str(self.resolution)])])
self.num_conv += len(self.extra_convs)
else:
self.extra_convs = None
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=self.up,
resample_filter=resample_filter, channels_last=self.channels_last)
def forward(self, x, img, ws, t=None, motion_w=None, 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 (isinstance(x, Tensor) and int(x.shape[0]) == 1))
# Input.
if self.in_channels == 0:
conv1_w = next(w_iter)
x = self.input(ws.shape[0], conv1_w, t=t, motion_w=motion_w, device=ws.device, dtype=dtype, memory_format=memory_format)
else:
misc.assert_shape(x, [None, self.in_channels, self.input_resolution, self.input_resolution])
x = x.to(dtype=dtype, memory_format=memory_format)
x = maybe_upsample(x, self.cfg.fmm_upsampling_mode, self.up) if self.use_fmm else x # [batch_size, c, h, w]
# Main layers.
if self.in_channels == 0:
x = x if self.time_fuser is None else self.time_fuser(x, t=t, motion_w=motion_w) # [batch_size, c + time_emb_dim, h, w]
x = self.conv1(x, conv1_w, 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:
conv0_w = next(w_iter)
if self.coord_fuser is not None:
x = self.coord_fuser(x, conv0_w, t=t, dtype=dtype, memory_format=memory_format)
if self.time_fuser is not None:
x = self.time_fuser(x, t=t, motion_w=motion_w) # [b, c + coord_dim + time_dim, h, w]
x = self.conv0(x, conv0_w, fused_modconv=fused_modconv, **layer_kwargs)
x = self.conv1(x, next(w_iter), fused_modconv=fused_modconv, **layer_kwargs)
if not self.extra_convs is None:
for conv, w in zip(self.extra_convs, w_iter):
x = conv(x, w, fused_modconv=fused_modconv, **layer_kwargs)
# ToRGB.
if img is not None:
misc.assert_shape(img, [None, self.img_channels, self.input_resolution, self.input_resolution])
if self.up == 2:
if self.use_fmm:
img = maybe_upsample(img, self.cfg.fmm_upsampling_mode, 2)
else:
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
#----------------------------------------------------------------------------
@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 = 0, # Use FP16 for the N highest resolutions.
cfg = {}, # Additional config
**block_kwargs, # Arguments for SynthesisBlock.
):
assert img_resolution >= 4 and img_resolution & (img_resolution - 1) == 0
super().__init__()
self.w_dim = w_dim
self.cfg = cfg
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(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)
if self.cfg.motion.w_dim > 0:
self.motion_encoder = MotionEncoder(self.cfg, resolutions=self.block_resolutions)
self.motion_w_dim = self.motion_encoder.get_output_dim()
else:
self.motion_encoder = None
self.motion_w_dim = 0
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=self.w_dim + (self.motion_w_dim if self.cfg.time_enc.cond_type == 'concat_w' else 0),
motion_w_dim=self.motion_w_dim,
resolution=res,
img_channels=img_channels,
is_last=is_last,
use_fp16=use_fp16,
cfg=cfg,
**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, t=None, c=None, l=None, motion_noise=None, motion_w=None, **block_kwargs):
assert len(ws) == len(c) == len(t), f"Wrong shape: {ws.shape}, {c.shape}, {t.shape}"
assert t.ndim == 2, f"Wrong shape: {t.shape}"
misc.assert_shape(ws, [None, self.num_ws, self.w_dim])
block_ws = []
if self.motion_encoder is None:
ws = ws.repeat_interleave(t.shape[1], dim=0) # [batch_size * num_frames, num_ws, w_dim]
motion_w = None
else:
if motion_w is None:
motion_info = self.motion_encoder(c, t, l=l, w=ws[:, 0], motion_noise=motion_noise) # [batch_size * num_frames, motion_w_dim]
motion_w = motion_info['motion_w'] # [batch_size * num_frames, motion_w_dim]
if not self.cfg.time_enc.per_resolution and self.cfg.time_enc.cond_type in ['concat_w', 'sum_w']:
misc.assert_shape(motion_w, [t.shape[0] * t.shape[1], self.motion_w_dim])
if self.cfg.time_enc.cond_type == 'concat_w':
motion_ws = motion_w.unsqueeze(1).repeat(1, self.num_ws, 1) # [batch_size * num_frames, num_ws, motion_w_dim]
ws = torch.cat([ws.repeat_interleave(t.shape[1], dim=0), motion_ws], dim=2) # [batch_size * num_frames, num_ws, w_dim + motion_w_dim]
elif self.cfg.time_enc.cond_type == 'sum_w':
ws = ws.repeat_interleave(t.shape[1], dim=0) + motion_w.unsqueeze(1) # [batch_size * num_frames, num_ws, w_dim + motion_w_dim]
else:
ws = ws.repeat_interleave(t.shape[1], dim=0) # [batch_size * num_frames, num_ws, w_dim]
with torch.autograd.profiler.record_function('split_ws'):
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}')
if self.cfg.time_enc.per_resolution:
motion_w = motion_info['motion_w'][res] # [batch_size * num_frames, motion_w_dim]
if self.cfg.time_enc.cond_type == "concat_w":
cur_ws = torch.cat([cur_ws, motion_w.unsqueeze(1).repeat(1, cur_ws.shape[1], 1)], dim=2) # [batch_size * num_frames, num_cur_ws, w_dim + motion_w_dim]
elif self.cfg.time_enc.cond_type == "sum_w":
cur_ws = cur_ws + motion_w.unsqueeze(1) # [batch_size * num_frames, num_cur_ws, w_dim]
elif self.cfg.time_enc.cond_type == "concat_act":
pass
else:
raise NotImplementedError(f"Unkown agg op: {self.cfg.motion.agg}")
if self.cfg.time_enc.cond_type != 'concat_act':
motion_w = None # To make sure that we do not leak.
x, img = block(x, img, cur_ws, t=t, motion_w=motion_w, **block_kwargs)
return img
#----------------------------------------------------------------------------
@persistence.persistent_class
class Generator(torch.nn.Module):
def __init__(self,
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.
cfg = {}, # Config
):
super().__init__()
self.cfg = cfg
self.sampling_dict = OmegaConf.to_container(OmegaConf.create({**self.cfg.sampling}))
self.z_dim = self.cfg.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, cfg=cfg, **synthesis_kwargs)
self.num_ws = self.synthesis.num_ws
self.mapping = MappingNetwork(z_dim=self.z_dim, c_dim=c_dim, w_dim=w_dim, num_ws=self.num_ws, **mapping_kwargs)
def forward(self, z, c, t, l, truncation_psi=1, truncation_cutoff=None, **synthesis_kwargs):
assert len(z) == len(c) == len(t), f"Wrong shape: {z.shape}, {c.shape}, {t.shape}"
assert t.ndim == 2, f"Wrong shape: {t.shape}"
ws = self.mapping(z, c, l=l, truncation_psi=truncation_psi, truncation_cutoff=truncation_cutoff) # [batch_size, num_ws, w_dim]
img = self.synthesis(ws, t=t, c=c, l=l, **synthesis_kwargs) # [batch_size * num_frames, c, h, w]
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.
c_dim = 0, # Embedding size for t.
cfg = {}, # Main config.
):
assert architecture in ['orig', 'skip', 'resnet']
super().__init__()
self.cfg = cfg
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()
conv0_in_channels = in_channels if in_channels > 0 else tmp_channels
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)
if self.cfg.hyper_type in ['hyper', 'dummy_hyper']:
assert next(trainable_iter)
self.conv0 = SynthesisLayer(
conv0_in_channels,
tmp_channels,
w_dim=c_dim,
resolution=self.resolution,
kernel_size=3,
activation=activation,
conv_clamp=conv_clamp,
channels_last=self.channels_last,
cfg=self.cfg.dummy_synth_cfg)
elif self.cfg.hyper_type == 'no_hyper':
self.conv0 = Conv2dLayer(conv0_in_channels, tmp_channels, kernel_size=3, activation=activation,
trainable=next(trainable_iter), conv_clamp=conv_clamp, channels_last=self.channels_last)
else:
raise NotImplementedError("Unknown hyper type:", self.cfg.hyper_type)
if int(self.resolution) in [int(r) for r in self.cfg.contr.get('resolutions', [])] and self.cfg.num_frames_per_sample > 1:
assert self.cfg.agg.type != "concat" or self.cfg.agg.concat_res < self.resolution, \
f"Cant compute similarities after concatenation: {self.cfg.agg.concat_res} > {self.resolution}"
self.contr = GroupwiseContrastiveLayer(
cfg=self.cfg, in_channels=conv0_in_channels, c_dim=c_dim, resolution=self.resolution,
conv_clamp=conv_clamp, channels_last=self.channels_last)
conv1_in_channels = self.contr.get_output_dim()
else:
self.contr = None
conv1_in_channels = tmp_channels
self.conv1 = Conv2dLayer(conv1_in_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(conv0_in_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, c, 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:
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
c_weight = 1.0 if self.cfg.get('is_hyper', True) else 0.0
if self.cfg.hyper_type == 'hyper':
cond_kwargs = {'w': c}
elif self.cfg.hyper_type == 'dummy_hyper':
cond_kwargs = {'w': c * 0.0}
elif self.cfg.hyper_type == 'no_hyper':
cond_kwargs = {}
else:
raise NotImplementedError("Unknwon hyper type", self.cfg.hyper_type)
# Main layers.
if self.architecture == 'resnet':
y = self.skip(x, gain=np.sqrt(0.5))
x = self.conv0(x, **cond_kwargs)
x = x if self.contr is None else self.contr(x, c)
x = self.conv1(x, gain=np.sqrt(0.5))
x = y.add_(x)
else:
x = self.conv0(x, **cond_kwargs)
x = x if self.contr is None else self.contr(x, c)
x = self.conv1(x)
assert x.dtype == dtype
return x, img
#----------------------------------------------------------------------------
@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) # [N(C+1)HW] Append to input as new channels.
return x
#----------------------------------------------------------------------------
@persistence.persistent_class
class GroupwiseContrastiveLayer(torch.nn.Module):
"""
This layer compares images of the same video with one another
and concatenates the similarity scores back to their original activations
"""
def __init__(self, cfg, in_channels: int, resolution: int, c_dim: int, conv_clamp: int=None, channels_last: bool=False):
super().__init__()
self.cfg = cfg
self.in_channels = in_channels
self.group_size = self.cfg.num_frames_per_sample
self.dim = self.cfg.contr.dim
self.scale = 1 if self.dim <= 3 else ((self.dim - 2) ** 2 / self.dim) ** 0.5
self.diff_based = self.cfg.contr.get('diff_based', False)
self.transform = SynthesisLayer(
in_channels=in_channels,
out_channels=self.cfg.contr.dim,
w_dim=c_dim,
resolution=resolution,
kernel_size=self.cfg.contr.kernel_size,
activation='lrelu',
conv_clamp=conv_clamp,
channels_last=channels_last,
cfg=self.cfg.dummy_synth_cfg,
)
self.agg = self.cfg.contr.agg
def get_output_dim(self) -> int:
if self.agg in ["mean", "min", "max", "fmin_rmax"]:
return self.in_channels + 1
elif self.agg == "none":
if self.diff_based:
return self.in_channels + (self.group_size - 1) * (self.group_size - 2)
else:
return self.in_channels + self.group_size - 1
else:
raise NotImplementedError
def forward(self, x: Tensor, c: Tensor) -> Tensor:
bn, c_in, h, w = x.shape
num_groups = bn // self.group_size
y = self.transform(x, c) # [bn, dim, h, w]
y = y.reshape(num_groups, self.group_size, self.dim, h, w) # [num_groups, group_size, dim, h, w]
if self.diff_based:
# We first compute differences between frames features
# and then we compute similarities between those vectors
# This should show D how the pixels moved
d = y[:, 1:] - y[:, :-1] # [num_groups, group_size - 1, dim, h, w]
# Unfortunately, we cannot normalize the diffs because R1 penalty falls with NaNs...
# So, normalize the scale at least somehow...
d_sims = (1.0 / np.sqrt(d.shape[2])) * d.permute(0, 3, 4, 1, 2) @ d.permute(0, 3, 4, 2, 1) # [num_groups, h, w, group_size - 1, group_size - 1]
assert not torch.isnan(d_sims).any(), "There are NaNs in the diffs tensor"
d_sims = remove_diag(d_sims) # [num_groups, h, w, group_size - 1, group_size - 2]
y = d_sims.unsqueeze(1).repeat(1, self.group_size, 1, 1, 1, 1) # [num_groups, group_size, h, w, group_size - 1, group_size - 2]
y = y.view(bn, h, w, (self.group_size - 1) * (self.group_size - 2)).permute(0, 3, 1, 2) # [bn, (group_size - 1) * (group_size - 2), h, w]
else:
y = F.normalize(y, dim=2) # [num_groups, group_size, dim, h, w]
y = y.permute(0, 3, 4, 1, 2) @ y.permute(0, 3, 4, 2, 1) # [num_groups, h, w, group_size, group_size]
y = y * self.scale # [num_groups, h, w, group_size, group_size]
y = remove_diag(y) # [num_groups, h, w, group_size, group_size - 1]
y = y.permute(0, 3, 4, 1, 2) # [num_groups, group_size, group_size - 1, h, w]
y = y.reshape(bn, self.group_size - 1, h, w) # [bn, group_size - 1, h, w]
if self.agg == "mean":
y = y.mean(dim=1, keepdim=True) # [bn, 1, h, w]
elif self.agg == "max":
y = y.max(dim=1, keepdim=True)[0] # [bn, 1, h, w]
elif self.agg == "min":
y = y.min(dim=1, keepdim=True)[0] # [bn, 1, h, w]
elif self.agg == "none":
y = y # [bn, group_size - 1, h, w]
else:
raise NotImplementedError
y = torch.cat([x, y], dim=1) # [bn, in_channel + d, h, w]
return y
#----------------------------------------------------------------------------
@persistence.persistent_class
class FeatDiffLayer(torch.nn.Module):
"""
Computes differences between consecutive frames features
"""
def __init__(self, cfg, in_channels: int, dim: int, resolution: int, c_dim: int, conv_clamp: int=None, channels_last: bool=False):
super().__init__()
self.cfg = cfg
self.in_channels = in_channels
self.group_size = self.cfg.num_frames_per_sample
self.dim = dim
self.transform = SynthesisLayer(
in_channels=in_channels,
out_channels=self.dim,
w_dim=c_dim,
resolution=resolution,
kernel_size=3,
activation='lrelu',
conv_clamp=conv_clamp,
channels_last=channels_last,
cfg=self.cfg.dummy_synth_cfg,
)
def get_output_dim(self) -> int:
return (self.group_size - 1) * self.dim
def forward(self, x: Tensor, c: Tensor) -> Tensor:
bn, c_in, h, w = x.shape
num_groups = bn // self.group_size
y = self.transform(x, c) # [bn, dim, h, w]
y = y.reshape(num_groups, self.group_size, self.dim, h, w) # [num_groups, group_size, dim, h, w]
d = y[:, 1:] - y[:, :-1] # [num_groups, group_size - 1, dim, h, w]
d = d.view(num_groups, (self.group_size - 1) * self.dim, h, w) # [num_groups, (group_size - 1) * c, h, w]
return 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.
cfg = {}, # Architecture config.
):
assert architecture in ['orig', 'skip', 'resnet']
super().__init__()
self.cfg = cfg
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)
if self.cfg.predict_dists_weight > 0.0:
self.dist_predictor = nn.Sequential(
FullyConnectedLayer(in_channels * (resolution ** 2), in_channels, activation=activation),
torch.nn.Flatten(),
FullyConnectedLayer(in_channels, get_max_dist(self.cfg.sampling), activation='linear'),
)
else:
self.dist_predictor = None
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)
dist_preds = None if self.dist_predictor is None else self.dist_predictor(x.flatten(1)) # [batch_size]
x = self.fc(x.flatten(1))
x = self.out(x) # [batch_size, out_dim]
if not self.dist_predictor is None:
# If one uncomments this, then we'll encounter a DDP consistency error for some reason
x = x + dist_preds.sum() * 0.0
# 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)) # [batch_size, 1]
assert x.dtype == dtype
return x, dist_preds
#----------------------------------------------------------------------------
@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 = 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.
block_kwargs = {}, # Arguments for DiscriminatorBlock.
mapping_kwargs = {}, # Arguments for MappingNetwork.
epilogue_kwargs = {}, # Arguments for DiscriminatorEpilogue.
cfg = {}, # Additional config.
):
super().__init__()
self.cfg = cfg
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 self.cfg.num_frames_per_sample > 1:
if self.cfg.time_enc_type == 'diff':
self.time_encoder = TemporalDifferenceEncoder(self.cfg)
elif self.cfg.time_enc_type == 'multi':
self.time_encoder = MultiTimeEncoder(self.cfg)
elif self.cfg.time_enc_type == 'joint':
self.time_encoder = JointTimeEncoder(self.cfg)
else:
raise NotImplementedError(f"Unknown time encoder in D: {self.cfg.time_enc_type}")
assert self.time_encoder.get_total_dim() > 0
else:
self.time_encoder = None
if self.c_dim == 0 and self.time_encoder is None:
cmap_dim = 0
common_kwargs = dict(img_channels=img_channels, architecture=architecture, conv_clamp=conv_clamp)
conditioning_dim = c_dim + (0 if self.time_encoder is None else self.time_encoder.get_total_dim())
cur_layer_idx = 0
if self.cfg.agg.type == "concat" and self.cfg.agg.get('concat_diff_dim', 0) > 0:
self.diff_transform = FeatDiffLayer(
cfg=self.cfg,
in_channels=channels_dict[self.cfg.agg.concat_res] // self.cfg.num_frames_div_factor,
dim=self.cfg.agg.concat_diff_dim,
resolution=self.cfg.agg.concat_res,
c_dim=conditioning_dim,
conv_clamp=conv_clamp)
else:
self.diff_transform = None
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]
if self.cfg.agg.type == "concat":
# Adjust numbers of channels
if res // 2 == self.cfg.agg.concat_res:
out_channels = out_channels // self.cfg.num_frames_div_factor
if res == self.cfg.agg.concat_res:
in_channels = (in_channels // self.cfg.num_frames_div_factor) * self.cfg.num_frames_per_sample
in_channels += (0 if self.diff_transform is None else self.diff_transform.get_output_dim())
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, cfg=self.cfg, c_dim=conditioning_dim, **block_kwargs, **common_kwargs)
setattr(self, f'b{res}', block)
cur_layer_idx += block.num_layers
if self.c_dim > 0 or not self.time_encoder is None:
self.mapping = MappingNetwork(z_dim=0, c_dim=conditioning_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, cfg=self.cfg, **epilogue_kwargs, **common_kwargs)
def forward(self, img, c, t, **block_kwargs):
# TODO: pass img in [b, c, t, h, w] shape instead of [b * t, c, h, w]
assert len(img) == t.shape[0] * t.shape[1], f"Wrong shape: {img.shape}, {t.shape}"
assert t.ndim == 2, f"Wrong shape: {t.shape}"
if not self.time_encoder is None:
t_embs = self.time_encoder(t.view(-1, self.cfg.num_frames_per_sample)) # [batch_size, t_dim]
c_orig = torch.cat([c, t_embs], dim=1) # [batch_size, c_dim + t_dim]
c = c_orig.repeat_interleave(t.shape[1], dim=0) # [batch_size * num_frames, c_dim + t_dim]
if self.cfg.dummy_c:
c = c * 0.0
c_orig = c_orig * 0.0
x = None
for res in self.block_resolutions:
block = getattr(self, f'b{res}')
if self.cfg.agg.type == "concat" and res == self.cfg.agg.concat_res:
d = None if self.diff_transform is None else self.diff_transform(x, c) # [batch_size, num_frames - 1, diff_c, h, w]
x = x.view(-1, self.cfg.num_frames_per_sample, *x.shape[1:]) # [batch_size, num_frames, c, h, w]
x = x.view(x.shape[0], -1, *x.shape[3:]) # [batch_size, num_frames * c, h, w]
x = x if self.diff_transform is None else torch.cat([x, d], dim=1) # [batch_size, num_frames * c + (num_frames - 1) * d_dim, h, w]
c = c_orig
x, img = block(x, img, c, **block_kwargs)
cmap = None
if self.c_dim > 0 or not self.time_encoder is None:
assert c.shape[1] > 0
if c.shape[1] > 0:
cmap = self.mapping(None, c)
x, dist_preds = self.b4(x, img, cmap)
x = x.squeeze(1) # [batch_size]
return {'image_logits': x, 'dist_preds': dist_preds}
#----------------------------------------------------------------------------
from stylegan-v.
Thanks for your reply!
from stylegan-v.
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- Error: batch_size should be a positive integer value, but got batch_size=0 HOT 1
- FVD Metrics HOT 1
- FaceForensics download
- The frame sequence is out of order when evaluating fvd
- I'd like to know how the number 2048 in fvd2048_16f is determined?
- Could you please add an Apache 2.0 license to your project to facilitate downstream repositories using your code?
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