DL-Art-School/codes/models/archs/stylegan/stylegan2_unet_disc.py
2020-11-14 22:04:48 -07:00

124 lines
3.7 KiB
Python

from functools import partial
from math import log2
import torch
import torch.nn as nn
def leaky_relu(p=0.2):
return nn.LeakyReLU(p)
def double_conv(chan_in, chan_out):
return nn.Sequential(
nn.Conv2d(chan_in, chan_out, 3, padding=1),
leaky_relu(),
nn.Conv2d(chan_out, chan_out, 3, padding=1),
leaky_relu()
)
class DownBlock(nn.Module):
def __init__(self, input_channels, filters, downsample=True):
super().__init__()
self.conv_res = nn.Conv2d(input_channels, filters, 1, stride = (2 if downsample else 1))
self.net = double_conv(input_channels, filters)
self.down = nn.Conv2d(filters, filters, 3, padding = 1, stride = 2) if downsample else None
def forward(self, x):
res = self.conv_res(x)
x = self.net(x)
unet_res = x
if self.down is not None:
x = self.down(x)
x = x + res
return x, unet_res
class UpBlock(nn.Module):
def __init__(self, input_channels, filters):
super().__init__()
self.conv_res = nn.ConvTranspose2d(input_channels // 2, filters, 1, stride = 2)
self.net = double_conv(input_channels, filters)
self.up = nn.Upsample(scale_factor = 2, mode='bilinear', align_corners=False)
self.input_channels = input_channels
self.filters = filters
def forward(self, x, res):
*_, h, w = x.shape
conv_res = self.conv_res(x, output_size = (h * 2, w * 2))
x = self.up(x)
x = torch.cat((x, res), dim=1)
x = self.net(x)
x = x + conv_res
return x
class StyleGan2UnetDiscriminator(nn.Module):
def __init__(self, image_size, network_capacity = 16, fmap_max = 512, input_filters=3):
super().__init__()
num_layers = int(log2(image_size) - 3)
blocks = []
filters = [input_filters] + [(network_capacity) * (2 ** i) for i in range(num_layers + 1)]
set_fmap_max = partial(min, fmap_max)
filters = list(map(set_fmap_max, filters))
filters[-1] = filters[-2]
chan_in_out = list(zip(filters[:-1], filters[1:]))
chan_in_out = list(map(list, chan_in_out))
down_blocks = []
attn_blocks = []
for ind, (in_chan, out_chan) in enumerate(chan_in_out):
num_layer = ind + 1
is_not_last = ind != (len(chan_in_out) - 1)
block = DownBlock(in_chan, out_chan, downsample = is_not_last)
down_blocks.append(block)
attn_fn = attn_and_ff(out_chan)
attn_blocks.append(attn_fn)
self.down_blocks = nn.ModuleList(down_blocks)
self.attn_blocks = nn.ModuleList(attn_blocks)
last_chan = filters[-1]
self.to_logit = nn.Sequential(
leaky_relu(),
nn.AvgPool2d(image_size // (2 ** num_layers)),
Flatten(1),
nn.Linear(last_chan, 1)
)
self.conv = double_conv(last_chan, last_chan)
dec_chan_in_out = chan_in_out[:-1][::-1]
self.up_blocks = nn.ModuleList(list(map(lambda c: UpBlock(c[1] * 2, c[0]), dec_chan_in_out)))
self.conv_out = nn.Conv2d(3, 1, 1)
def forward(self, x):
b, *_ = x.shape
residuals = []
for (down_block, attn_block) in zip(self.down_blocks, self.attn_blocks):
x, unet_res = down_block(x)
residuals.append(unet_res)
if attn_block is not None:
x = attn_block(x)
x = self.conv(x) + x
enc_out = self.to_logit(x)
for (up_block, res) in zip(self.up_blocks, residuals[:-1][::-1]):
x = up_block(x, res)
dec_out = self.conv_out(x)
return enc_out.squeeze(), dec_out