BerfScene / models /stylegan_discriminator.py
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# python3.7
"""Contains the implementation of discriminator described in StyleGAN.
Paper: https://arxiv.org/pdf/1812.04948.pdf
Official TensorFlow implementation: https://github.com/NVlabs/stylegan
"""
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.cuda.amp import autocast
__all__ = ['StyleGANDiscriminator']
# Resolutions allowed.
_RESOLUTIONS_ALLOWED = [8, 16, 32, 64, 128, 256, 512, 1024]
# Fused-scale options allowed.
_FUSED_SCALE_ALLOWED = [True, False, 'auto']
# pylint: disable=missing-function-docstring
class StyleGANDiscriminator(nn.Module):
"""Defines the discriminator network in StyleGAN.
NOTE: The discriminator takes images with `RGB` channel order and pixel
range [-1, 1] as inputs.
Settings for the backbone:
(1) resolution: The resolution of the input image. (default: -1)
(2) init_res: Smallest resolution of the convolutional backbone.
(default: 4)
(3) image_channels: Number of channels of the input image. (default: 3)
(4) fused_scale: The strategy of fusing `conv2d` and `downsample` as one
operator. `True` means blocks from all resolutions will fuse. `False`
means blocks from all resolutions will not fuse. `auto` means blocks
from resolutions higher than (or equal to) `fused_scale_res` will fuse.
(default: `auto`)
(5) fused_scale_res: Minimum resolution to fuse `conv2d` and `downsample`
as one operator. This field only takes effect if `fused_scale` is set
as `auto`. (default: 128)
(6) use_wscale: Whether to use weight scaling. (default: True)
(7) wscale_gain: The factor to control weight scaling. (default: sqrt(2.0))
(8) lr_mul: Learning rate multiplier for backbone. (default: 1.0)
(9) mbstd_groups: Group size for the minibatch standard deviation layer.
`0` means disable. (default: 4)
(10) mbstd_channels: Number of new channels (appended to the original
feature map) after the minibatch standard deviation layer. (default: 1)
(11) fmaps_base: Factor to control number of feature maps for each layer.
(default: 16 << 10)
(12) fmaps_max: Maximum number of feature maps in each layer. (default: 512)
(13) filter_kernel: Kernel used for filtering (e.g., downsampling).
(default: (1, 2, 1))
(14) eps: A small value to avoid divide overflow. (default: 1e-8)
Settings for conditional model:
(1) label_dim: Dimension of the additional label for conditional generation.
In one-hot conditioning case, it is equal to the number of classes. If
set to 0, conditioning training will be disabled. (default: 0)
Runtime settings:
(1) enable_amp: Whether to enable automatic mixed precision training.
(default: False)
"""
def __init__(self,
# Settings for backbone.
resolution=-1,
init_res=4,
image_channels=3,
fused_scale='auto',
fused_scale_res=128,
use_wscale=True,
wscale_gain=np.sqrt(2.0),
lr_mul=1.0,
mbstd_groups=4,
mbstd_channels=1,
fmaps_base=16 << 10,
fmaps_max=512,
filter_kernel=(1, 2, 1),
eps=1e-8,
# Settings for conditional model.
label_dim=0):
"""Initializes with basic settings.
Raises:
ValueError: If the `resolution` is not supported, or `fused_scale`
is not supported.
"""
super().__init__()
if resolution not in _RESOLUTIONS_ALLOWED:
raise ValueError(f'Invalid resolution: `{resolution}`!\n'
f'Resolutions allowed: {_RESOLUTIONS_ALLOWED}.')
if fused_scale not in _FUSED_SCALE_ALLOWED:
raise ValueError(f'Invalid fused-scale option: `{fused_scale}`!\n'
f'Options allowed: {_FUSED_SCALE_ALLOWED}.')
self.init_res = init_res
self.init_res_log2 = int(np.log2(init_res))
self.resolution = resolution
self.final_res_log2 = int(np.log2(resolution))
self.image_channels = image_channels
self.fused_scale = fused_scale
self.fused_scale_res = fused_scale_res
self.use_wscale = use_wscale
self.wscale_gain = wscale_gain
self.lr_mul = lr_mul
self.mbstd_groups = mbstd_groups
self.mbstd_channels = mbstd_channels
self.fmaps_base = fmaps_base
self.fmaps_max = fmaps_max
self.filter_kernel = filter_kernel
self.eps = eps
self.label_dim = label_dim
# Level-of-details (used for progressive training).
self.register_buffer('lod', torch.zeros(()))
self.pth_to_tf_var_mapping = {'lod': 'lod'}
for res_log2 in range(self.final_res_log2, self.init_res_log2 - 1, -1):
res = 2 ** res_log2
in_channels = self.get_nf(res)
out_channels = self.get_nf(res // 2)
block_idx = self.final_res_log2 - res_log2
# Input convolution layer for each resolution.
self.add_module(
f'input{block_idx}',
ConvLayer(in_channels=image_channels,
out_channels=in_channels,
kernel_size=1,
add_bias=True,
scale_factor=1,
fused_scale=False,
filter_kernel=None,
use_wscale=use_wscale,
wscale_gain=wscale_gain,
lr_mul=lr_mul,
activation_type='lrelu'))
self.pth_to_tf_var_mapping[f'input{block_idx}.weight'] = (
f'FromRGB_lod{block_idx}/weight')
self.pth_to_tf_var_mapping[f'input{block_idx}.bias'] = (
f'FromRGB_lod{block_idx}/bias')
# Convolution block for each resolution (except the last one).
if res != self.init_res:
# First layer (kernel 3x3) without downsampling.
layer_name = f'layer{2 * block_idx}'
self.add_module(
layer_name,
ConvLayer(in_channels=in_channels,
out_channels=in_channels,
kernel_size=3,
add_bias=True,
scale_factor=1,
fused_scale=False,
filter_kernel=None,
use_wscale=use_wscale,
wscale_gain=wscale_gain,
lr_mul=lr_mul,
activation_type='lrelu'))
self.pth_to_tf_var_mapping[f'{layer_name}.weight'] = (
f'{res}x{res}/Conv0/weight')
self.pth_to_tf_var_mapping[f'{layer_name}.bias'] = (
f'{res}x{res}/Conv0/bias')
# Second layer (kernel 3x3) with downsampling
layer_name = f'layer{2 * block_idx + 1}'
self.add_module(
layer_name,
ConvLayer(in_channels=in_channels,
out_channels=out_channels,
kernel_size=3,
add_bias=True,
scale_factor=2,
fused_scale=(res >= fused_scale_res
if fused_scale == 'auto'
else fused_scale),
filter_kernel=filter_kernel,
use_wscale=use_wscale,
wscale_gain=wscale_gain,
lr_mul=lr_mul,
activation_type='lrelu'))
self.pth_to_tf_var_mapping[f'{layer_name}.weight'] = (
f'{res}x{res}/Conv1_down/weight')
self.pth_to_tf_var_mapping[f'{layer_name}.bias'] = (
f'{res}x{res}/Conv1_down/bias')
# Convolution block for last resolution.
else:
self.mbstd = MiniBatchSTDLayer(groups=mbstd_groups,
new_channels=mbstd_channels,
eps=eps)
# First layer (kernel 3x3) without downsampling.
layer_name = f'layer{2 * block_idx}'
self.add_module(
layer_name,
ConvLayer(in_channels=in_channels + mbstd_channels,
out_channels=in_channels,
kernel_size=3,
add_bias=True,
scale_factor=1,
fused_scale=False,
filter_kernel=None,
use_wscale=use_wscale,
wscale_gain=wscale_gain,
lr_mul=lr_mul,
activation_type='lrelu'))
self.pth_to_tf_var_mapping[f'{layer_name}.weight'] = (
f'{res}x{res}/Conv/weight')
self.pth_to_tf_var_mapping[f'{layer_name}.bias'] = (
f'{res}x{res}/Conv/bias')
# Second layer, as a fully-connected layer.
layer_name = f'layer{2 * block_idx + 1}'
self.add_module(
f'layer{2 * block_idx + 1}',
DenseLayer(in_channels=in_channels * res * res,
out_channels=in_channels,
add_bias=True,
use_wscale=use_wscale,
wscale_gain=wscale_gain,
lr_mul=lr_mul,
activation_type='lrelu'))
self.pth_to_tf_var_mapping[f'{layer_name}.weight'] = (
f'{res}x{res}/Dense0/weight')
self.pth_to_tf_var_mapping[f'{layer_name}.bias'] = (
f'{res}x{res}/Dense0/bias')
# Final dense layer to output score.
self.output = DenseLayer(in_channels=in_channels,
out_channels=max(label_dim, 1),
add_bias=True,
use_wscale=use_wscale,
wscale_gain=1.0,
lr_mul=lr_mul,
activation_type='linear')
self.pth_to_tf_var_mapping['output.weight'] = (
f'{res}x{res}/Dense1/weight')
self.pth_to_tf_var_mapping['output.bias'] = (
f'{res}x{res}/Dense1/bias')
def get_nf(self, res):
"""Gets number of feature maps according to the given resolution."""
return min(self.fmaps_base // res, self.fmaps_max)
def forward(self, image, label=None, lod=None, enable_amp=False):
expected_shape = (self.image_channels, self.resolution, self.resolution)
if image.ndim != 4 or image.shape[1:] != expected_shape:
raise ValueError(f'The input tensor should be with shape '
f'[batch_size, channel, height, width], where '
f'`channel` equals to {self.image_channels}, '
f'`height`, `width` equal to {self.resolution}!\n'
f'But `{image.shape}` is received!')
lod = self.lod.item() if lod is None else lod
if lod + self.init_res_log2 > self.final_res_log2:
raise ValueError(f'Maximum level-of-details (lod) is '
f'{self.final_res_log2 - self.init_res_log2}, '
f'but `{lod}` is received!')
if self.label_dim:
if label is None:
raise ValueError(f'Model requires an additional label '
f'(with dimension {self.label_dim}) as input, '
f'but no label is received!')
batch = image.shape[0]
if (label.ndim != 2 or label.shape != (batch, self.label_dim)):
raise ValueError(f'Input label should be with shape '
f'[batch_size, label_dim], where '
f'`batch_size` equals to {batch}, and '
f'`label_dim` equals to {self.label_dim}!\n'
f'But `{label.shape}` is received!')
label = label.to(dtype=torch.float32)
with autocast(enabled=enable_amp):
for res_log2 in range(
self.final_res_log2, self.init_res_log2 - 1, -1):
block_idx = current_lod = self.final_res_log2 - res_log2
if current_lod <= lod < current_lod + 1:
x = getattr(self, f'input{block_idx}')(image)
elif current_lod - 1 < lod < current_lod:
alpha = lod - np.floor(lod)
y = getattr(self, f'input{block_idx}')(image)
x = y * alpha + x * (1 - alpha)
if lod < current_lod + 1:
if res_log2 == self.init_res_log2:
x = self.mbstd(x)
x = getattr(self, f'layer{2 * block_idx}')(x)
x = getattr(self, f'layer{2 * block_idx + 1}')(x)
if lod > current_lod:
image = F.avg_pool2d(
image, kernel_size=2, stride=2, padding=0)
x = self.output(x)
if self.label_dim:
x = (x * label).sum(dim=1, keepdim=True)
results = {
'score': x,
'label': label
}
return results
class MiniBatchSTDLayer(nn.Module):
"""Implements the minibatch standard deviation layer."""
def __init__(self, groups, new_channels, eps):
super().__init__()
self.groups = groups
self.new_channels = new_channels
self.eps = eps
def extra_repr(self):
return (f'groups={self.groups}, '
f'new_channels={self.new_channels}, '
f'epsilon={self.eps}')
def forward(self, x):
if self.groups <= 1 or self.new_channels < 1:
return x
N, C, H, W = x.shape
G = min(self.groups, N) # Number of groups.
nC = self.new_channels # Number of channel groups.
c = C // nC # Channels per channel group.
y = x.reshape(G, -1, nC, c, H, W) # [GnFcHW]
y = y - y.mean(dim=0) # [GnFcHW]
y = y.square().mean(dim=0) # [nFcHW]
y = (y + self.eps).sqrt() # [nFcHW]
y = y.mean(dim=(2, 3, 4)) # [nF]
y = y.reshape(-1, nC, 1, 1) # [nF11]
y = y.repeat(G, 1, H, W) # [NFHW]
x = torch.cat((x, y), dim=1) # [N(C+F)HW]
return x
class Blur(torch.autograd.Function):
"""Defines blur operation with customized gradient computation."""
@staticmethod
def forward(ctx, x, kernel):
assert kernel.shape[2] == 3 and kernel.shape[3] == 3
ctx.save_for_backward(kernel)
y = F.conv2d(input=x,
weight=kernel,
bias=None,
stride=1,
padding=1,
groups=x.shape[1])
return y
@staticmethod
def backward(ctx, dy):
kernel, = ctx.saved_tensors
dx = BlurBackPropagation.apply(dy, kernel)
return dx, None, None
class BlurBackPropagation(torch.autograd.Function):
"""Defines the back propagation of blur operation.
NOTE: This is used to speed up the backward of gradient penalty.
"""
@staticmethod
def forward(ctx, dy, kernel):
ctx.save_for_backward(kernel)
dx = F.conv2d(input=dy,
weight=kernel.flip((2, 3)),
bias=None,
stride=1,
padding=1,
groups=dy.shape[1])
return dx
@staticmethod
def backward(ctx, ddx):
kernel, = ctx.saved_tensors
ddy = F.conv2d(input=ddx,
weight=kernel,
bias=None,
stride=1,
padding=1,
groups=ddx.shape[1])
return ddy, None, None
class ConvLayer(nn.Module):
"""Implements the convolutional layer.
If downsampling is needed (i.e., `scale_factor = 2`), the feature map will
be filtered with `filter_kernel` first. If `fused_scale` is set as `True`,
`conv2d` and `downsample` will be fused as one operator, using stride
convolution.
"""
def __init__(self,
in_channels,
out_channels,
kernel_size,
add_bias,
scale_factor,
fused_scale,
filter_kernel,
use_wscale,
wscale_gain,
lr_mul,
activation_type):
"""Initializes with layer settings.
Args:
in_channels: Number of channels of the input tensor.
out_channels: Number of channels of the output tensor.
kernel_size: Size of the convolutional kernels.
add_bias: Whether to add bias onto the convolutional result.
scale_factor: Scale factor for downsampling. `1` means skip
downsampling.
fused_scale: Whether to fuse `conv2d` and `downsample` as one
operator, using stride convolution.
filter_kernel: Kernel used for filtering.
use_wscale: Whether to use weight scaling.
wscale_gain: Gain factor for weight scaling.
lr_mul: Learning multiplier for both weight and bias.
activation_type: Type of activation.
"""
super().__init__()
self.in_channels = in_channels
self.out_channels = out_channels
self.kernel_size = kernel_size
self.add_bias = add_bias
self.scale_factor = scale_factor
self.fused_scale = fused_scale
self.filter_kernel = filter_kernel
self.use_wscale = use_wscale
self.wscale_gain = wscale_gain
self.lr_mul = lr_mul
self.activation_type = activation_type
weight_shape = (out_channels, in_channels, kernel_size, kernel_size)
fan_in = kernel_size * kernel_size * in_channels
wscale = wscale_gain / np.sqrt(fan_in)
if use_wscale:
self.weight = nn.Parameter(torch.randn(*weight_shape) / lr_mul)
self.wscale = wscale * lr_mul
else:
self.weight = nn.Parameter(
torch.randn(*weight_shape) * wscale / lr_mul)
self.wscale = lr_mul
if add_bias:
self.bias = nn.Parameter(torch.zeros(out_channels))
self.bscale = lr_mul
else:
self.bias = None
if scale_factor > 1:
assert filter_kernel is not None
kernel = np.array(filter_kernel, dtype=np.float32).reshape(1, -1)
kernel = kernel.T.dot(kernel)
kernel = kernel / np.sum(kernel)
kernel = kernel[np.newaxis, np.newaxis]
self.register_buffer('filter', torch.from_numpy(kernel))
if scale_factor > 1 and fused_scale: # use stride convolution.
self.stride = scale_factor
else:
self.stride = 1
self.padding = kernel_size // 2
assert activation_type in ['linear', 'relu', 'lrelu']
def extra_repr(self):
return (f'in_ch={self.in_channels}, '
f'out_ch={self.out_channels}, '
f'ksize={self.kernel_size}, '
f'wscale_gain={self.wscale_gain:.3f}, '
f'bias={self.add_bias}, '
f'lr_mul={self.lr_mul:.3f}, '
f'downsample={self.scale_factor}, '
f'fused_scale={self.fused_scale}, '
f'downsample_filter={self.filter_kernel}, '
f'act={self.activation_type}')
def forward(self, x):
if self.scale_factor > 1:
# Disable `autocast` for customized autograd function.
# Please check reference:
# https://pytorch.org/docs/stable/notes/amp_examples.html#autocast-and-custom-autograd-functions
with autocast(enabled=False):
f = self.filter.repeat(self.in_channels, 1, 1, 1)
x = Blur.apply(x.float(), f) # Always use FP32.
weight = self.weight
if self.wscale != 1.0:
weight = weight * self.wscale
bias = None
if self.bias is not None:
bias = self.bias
if self.bscale != 1.0:
bias = bias * self.bscale
if self.scale_factor > 1 and self.fused_scale:
weight = F.pad(weight, (1, 1, 1, 1, 0, 0, 0, 0), 'constant', 0.0)
weight = (weight[:, :, 1:, 1:] + weight[:, :, :-1, 1:] +
weight[:, :, 1:, :-1] + weight[:, :, :-1, :-1]) * 0.25
x = F.conv2d(x,
weight=weight,
bias=bias,
stride=self.stride,
padding=self.padding)
if self.scale_factor > 1 and not self.fused_scale:
down = self.scale_factor
x = F.avg_pool2d(x, kernel_size=down, stride=down, padding=0)
if self.activation_type == 'linear':
pass
elif self.activation_type == 'relu':
x = F.relu(x, inplace=True)
elif self.activation_type == 'lrelu':
x = F.leaky_relu(x, negative_slope=0.2, inplace=True)
else:
raise NotImplementedError(f'Not implemented activation type '
f'`{self.activation_type}`!')
return x
class DenseLayer(nn.Module):
"""Implements the dense layer."""
def __init__(self,
in_channels,
out_channels,
add_bias,
use_wscale,
wscale_gain,
lr_mul,
activation_type):
"""Initializes with layer settings.
Args:
in_channels: Number of channels of the input tensor.
out_channels: Number of channels of the output tensor.
add_bias: Whether to add bias onto the fully-connected result.
use_wscale: Whether to use weight scaling.
wscale_gain: Gain factor for weight scaling.
lr_mul: Learning multiplier for both weight and bias.
activation_type: Type of activation.
"""
super().__init__()
self.in_channels = in_channels
self.out_channels = out_channels
self.add_bias = add_bias
self.use_wscale = use_wscale
self.wscale_gain = wscale_gain
self.lr_mul = lr_mul
self.activation_type = activation_type
weight_shape = (out_channels, in_channels)
wscale = wscale_gain / np.sqrt(in_channels)
if use_wscale:
self.weight = nn.Parameter(torch.randn(*weight_shape) / lr_mul)
self.wscale = wscale * lr_mul
else:
self.weight = nn.Parameter(
torch.randn(*weight_shape) * wscale / lr_mul)
self.wscale = lr_mul
if add_bias:
self.bias = nn.Parameter(torch.zeros(out_channels))
self.bscale = lr_mul
else:
self.bias = None
assert activation_type in ['linear', 'relu', 'lrelu']
def extra_repr(self):
return (f'in_ch={self.in_channels}, '
f'out_ch={self.out_channels}, '
f'wscale_gain={self.wscale_gain:.3f}, '
f'bias={self.add_bias}, '
f'lr_mul={self.lr_mul:.3f}, '
f'act={self.activation_type}')
def forward(self, x):
if x.ndim != 2:
x = x.flatten(start_dim=1)
weight = self.weight
if self.wscale != 1.0:
weight = weight * self.wscale
bias = None
if self.bias is not None:
bias = self.bias
if self.bscale != 1.0:
bias = bias * self.bscale
x = F.linear(x, weight=weight, bias=bias)
if self.activation_type == 'linear':
pass
elif self.activation_type == 'relu':
x = F.relu(x, inplace=True)
elif self.activation_type == 'lrelu':
x = F.leaky_relu(x, negative_slope=0.2, inplace=True)
else:
raise NotImplementedError(f'Not implemented activation type '
f'`{self.activation_type}`!')
return x
# pylint: enable=missing-function-docstring