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# python3.7
"""Contains the implementation of discriminator described in PGGAN.
Paper: https://arxiv.org/pdf/1710.10196.pdf
Official TensorFlow implementation:
https://github.com/tkarras/progressive_growing_of_gans
"""
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
__all__ = ['PGGANDiscriminator']
# Resolutions allowed.
_RESOLUTIONS_ALLOWED = [8, 16, 32, 64, 128, 256, 512, 1024]
# Default gain factor for weight scaling.
_WSCALE_GAIN = np.sqrt(2.0)
# pylint: disable=missing-function-docstring
class PGGANDiscriminator(nn.Module):
"""Defines the discriminator network in PGGAN.
NOTE: The discriminator takes images with `RGB` channel order and pixel
range [-1, 1] as inputs.
Settings for the network:
(1) resolution: The resolution of the input image.
(2) init_res: Smallest resolution of the convolutional backbone.
(default: 4)
(3) image_channels: Number of channels of the input image. (default: 3)
(4) 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)
(5) fused_scale: Whether to fused `conv2d` and `downsample` together,
resulting in `conv2d` with strides. (default: False)
(6) use_wscale: Whether to use weight scaling. (default: True)
(7) wscale_gain: The factor to control weight scaling. (default: sqrt(2.0))
(8) mbstd_groups: Group size for the minibatch standard deviation layer.
`0` means disable. (default: 16)
(9) fmaps_base: Factor to control number of feature maps for each layer.
(default: 16 << 10)
(10) fmaps_max: Maximum number of feature maps in each layer. (default: 512)
(11) eps: A small value to avoid divide overflow. (default: 1e-8)
"""
def __init__(self,
resolution,
init_res=4,
image_channels=3,
label_dim=0,
fused_scale=False,
use_wscale=True,
wscale_gain=np.sqrt(2.0),
mbstd_groups=16,
fmaps_base=16 << 10,
fmaps_max=512,
eps=1e-8):
"""Initializes with basic settings.
Raises:
ValueError: If the `resolution` is not supported.
"""
super().__init__()
if resolution not in _RESOLUTIONS_ALLOWED:
raise ValueError(f'Invalid resolution: `{resolution}`!\n'
f'Resolutions allowed: {_RESOLUTIONS_ALLOWED}.')
self.init_res = init_res
self.init_res_log2 = int(np.log2(self.init_res))
self.resolution = resolution
self.final_res_log2 = int(np.log2(self.resolution))
self.image_channels = image_channels
self.label_dim = label_dim
self.fused_scale = fused_scale
self.use_wscale = use_wscale
self.wscale_gain = wscale_gain
self.mbstd_groups = mbstd_groups
self.fmaps_base = fmaps_base
self.fmaps_max = fmaps_max
self.eps = eps
# 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=self.image_channels,
out_channels=in_channels,
kernel_size=1,
add_bias=True,
downsample=False,
fused_scale=False,
use_wscale=use_wscale,
wscale_gain=wscale_gain,
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:
self.add_module(
f'layer{2 * block_idx}',
ConvLayer(in_channels=in_channels,
out_channels=in_channels,
kernel_size=3,
add_bias=True,
downsample=False,
fused_scale=False,
use_wscale=use_wscale,
wscale_gain=wscale_gain,
activation_type='lrelu'))
tf_layer0_name = 'Conv0'
self.add_module(
f'layer{2 * block_idx + 1}',
ConvLayer(in_channels=in_channels,
out_channels=out_channels,
kernel_size=3,
add_bias=True,
downsample=True,
fused_scale=fused_scale,
use_wscale=use_wscale,
wscale_gain=wscale_gain,
activation_type='lrelu'))
tf_layer1_name = 'Conv1_down' if fused_scale else 'Conv1'
# Convolution block for last resolution.
else:
self.mbstd = MiniBatchSTDLayer(groups=mbstd_groups, eps=eps)
self.add_module(
f'layer{2 * block_idx}',
ConvLayer(
in_channels=in_channels + 1,
out_channels=in_channels,
kernel_size=3,
add_bias=True,
downsample=False,
fused_scale=False,
use_wscale=use_wscale,
wscale_gain=wscale_gain,
activation_type='lrelu'))
tf_layer0_name = 'Conv'
self.add_module(
f'layer{2 * block_idx + 1}',
DenseLayer(in_channels=in_channels * res * res,
out_channels=out_channels,
add_bias=True,
use_wscale=use_wscale,
wscale_gain=wscale_gain,
activation_type='lrelu'))
tf_layer1_name = 'Dense0'
self.pth_to_tf_var_mapping[f'layer{2 * block_idx}.weight'] = (
f'{res}x{res}/{tf_layer0_name}/weight')
self.pth_to_tf_var_mapping[f'layer{2 * block_idx}.bias'] = (
f'{res}x{res}/{tf_layer0_name}/bias')
self.pth_to_tf_var_mapping[f'layer{2 * block_idx + 1}.weight'] = (
f'{res}x{res}/{tf_layer1_name}/weight')
self.pth_to_tf_var_mapping[f'layer{2 * block_idx + 1}.bias'] = (
f'{res}x{res}/{tf_layer1_name}/bias')
# Final dense layer.
self.output = DenseLayer(in_channels=out_channels,
out_channels=1 + self.label_dim,
add_bias=True,
use_wscale=self.use_wscale,
wscale_gain=1.0,
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, lod=None):
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!')
lod = self.lod.item()
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)
return {'score': x}
class MiniBatchSTDLayer(nn.Module):
"""Implements the minibatch standard deviation layer."""
def __init__(self, groups, eps):
super().__init__()
self.groups = groups
self.eps = eps
def extra_repr(self):
return f'groups={self.groups}, epsilon={self.eps}'
def forward(self, x):
if self.groups <= 1:
return x
N, C, H, W = x.shape
G = min(self.groups, N) # Number of groups.
y = x.reshape(G, -1, C, H, W) # [GnCHW]
y = y - y.mean(dim=0) # [GnCHW]
y = y.square().mean(dim=0) # [nCHW]
y = (y + self.eps).sqrt() # [nCHW]
y = y.mean(dim=(1, 2, 3), keepdim=True) # [n111]
y = y.repeat(G, 1, H, W) # [N1HW]
x = torch.cat([x, y], dim=1) # [N(C+1)HW]
return x
class DownsamplingLayer(nn.Module):
"""Implements the downsampling layer.
Basically, this layer can be used to downsample feature maps with average
pooling.
"""
def __init__(self, scale_factor):
super().__init__()
self.scale_factor = scale_factor
def extra_repr(self):
return f'factor={self.scale_factor}'
def forward(self, x):
if self.scale_factor <= 1:
return x
return F.avg_pool2d(x,
kernel_size=self.scale_factor,
stride=self.scale_factor,
padding=0)
class ConvLayer(nn.Module):
"""Implements the convolutional layer.
Basically, this layer executes convolution, activation, and downsampling (if
needed) in sequence.
"""
def __init__(self,
in_channels,
out_channels,
kernel_size,
add_bias,
downsample,
fused_scale,
use_wscale,
wscale_gain,
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.
downsample: Whether to downsample the result after convolution.
fused_scale: Whether to fused `conv2d` and `downsample` together,
resulting in `conv2d` with strides.
use_wscale: Whether to use weight scaling.
wscale_gain: Gain factor for weight scaling.
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.downsample = downsample
self.fused_scale = fused_scale
self.use_wscale = use_wscale
self.wscale_gain = wscale_gain
self.activation_type = activation_type
if downsample and not fused_scale:
self.down = DownsamplingLayer(scale_factor=2)
else:
self.down = nn.Identity()
if downsample and fused_scale:
self.use_stride = True
self.stride = 2
self.padding = 1
else:
self.use_stride = False
self.stride = 1
self.padding = kernel_size // 2
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))
self.wscale = wscale
else:
self.weight = nn.Parameter(torch.randn(*weight_shape) * wscale)
self.wscale = 1.0
if add_bias:
self.bias = nn.Parameter(torch.zeros(out_channels))
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'ksize={self.kernel_size}, '
f'wscale_gain={self.wscale_gain:.3f}, '
f'bias={self.add_bias}, '
f'downsample={self.scale_factor}, '
f'fused_scale={self.fused_scale}, '
f'act={self.activation_type}')
def forward(self, x):
weight = self.weight
if self.wscale != 1.0:
weight = weight * self.wscale
if self.use_stride:
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=self.bias,
stride=self.stride,
padding=self.padding)
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}`!')
x = self.down(x)
return x
class DenseLayer(nn.Module):
"""Implements the dense layer."""
def __init__(self,
in_channels,
out_channels,
add_bias,
use_wscale,
wscale_gain,
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.
activation_type: Type of activation.
Raises:
NotImplementedError: If the `activation_type` is not supported.
"""
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.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))
self.wscale = wscale
else:
self.weight = nn.Parameter(torch.randn(*weight_shape) * wscale)
self.wscale = 1.0
if add_bias:
self.bias = nn.Parameter(torch.zeros(out_channels))
else:
self.bias = None
assert activation_type in ['linear', 'relu', 'lrelu']
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
x = F.linear(x, weight=weight, bias=self.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
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