forked from mrq/DL-Art-School
136 lines
3.9 KiB
Python
136 lines
3.9 KiB
Python
from functools import partial
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from math import log2
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import torch
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import torch.nn as nn
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from models.archs.stylegan.stylegan2 import attn_and_ff
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def leaky_relu(p=0.2):
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return nn.LeakyReLU(p)
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def double_conv(chan_in, chan_out):
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return nn.Sequential(
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nn.Conv2d(chan_in, chan_out, 3, padding=1),
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leaky_relu(),
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nn.Conv2d(chan_out, chan_out, 3, padding=1),
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leaky_relu()
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)
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class Flatten(nn.Module):
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def __init__(self, index):
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super().__init__()
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self.index = index
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def forward(self, x):
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return x.flatten(self.index)
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class DownBlock(nn.Module):
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def __init__(self, input_channels, filters, downsample=True):
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super().__init__()
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self.conv_res = nn.Conv2d(input_channels, filters, 1, stride = (2 if downsample else 1))
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self.net = double_conv(input_channels, filters)
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self.down = nn.Conv2d(filters, filters, 3, padding = 1, stride = 2) if downsample else None
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def forward(self, x):
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res = self.conv_res(x)
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x = self.net(x)
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unet_res = x
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if self.down is not None:
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x = self.down(x)
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x = x + res
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return x, unet_res
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class UpBlock(nn.Module):
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def __init__(self, input_channels, filters):
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super().__init__()
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self.conv_res = nn.ConvTranspose2d(input_channels // 2, filters, 1, stride = 2)
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self.net = double_conv(input_channels, filters)
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self.up = nn.Upsample(scale_factor = 2, mode='bilinear', align_corners=False)
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self.input_channels = input_channels
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self.filters = filters
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def forward(self, x, res):
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*_, h, w = x.shape
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conv_res = self.conv_res(x, output_size = (h * 2, w * 2))
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x = self.up(x)
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x = torch.cat((x, res), dim=1)
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x = self.net(x)
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x = x + conv_res
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return x
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class StyleGan2UnetDiscriminator(nn.Module):
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def __init__(self, image_size, network_capacity = 16, fmap_max = 512, input_filters=3):
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super().__init__()
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num_layers = int(log2(image_size) - 3)
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blocks = []
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filters = [input_filters] + [(network_capacity) * (2 ** i) for i in range(num_layers + 1)]
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set_fmap_max = partial(min, fmap_max)
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filters = list(map(set_fmap_max, filters))
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filters[-1] = filters[-2]
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chan_in_out = list(zip(filters[:-1], filters[1:]))
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chan_in_out = list(map(list, chan_in_out))
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down_blocks = []
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attn_blocks = []
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for ind, (in_chan, out_chan) in enumerate(chan_in_out):
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num_layer = ind + 1
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is_not_last = ind != (len(chan_in_out) - 1)
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block = DownBlock(in_chan, out_chan, downsample = is_not_last)
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down_blocks.append(block)
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attn_fn = attn_and_ff(out_chan)
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attn_blocks.append(attn_fn)
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self.down_blocks = nn.ModuleList(down_blocks)
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self.attn_blocks = nn.ModuleList(attn_blocks)
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last_chan = filters[-1]
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self.to_logit = nn.Sequential(
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leaky_relu(),
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nn.AvgPool2d(image_size // (2 ** num_layers)),
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Flatten(1),
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nn.Linear(last_chan, 1)
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)
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self.conv = double_conv(last_chan, last_chan)
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dec_chan_in_out = chan_in_out[:-1][::-1]
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self.up_blocks = nn.ModuleList(list(map(lambda c: UpBlock(c[1] * 2, c[0]), dec_chan_in_out)))
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self.conv_out = nn.Conv2d(input_filters, 1, 1)
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def forward(self, x):
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b, *_ = x.shape
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residuals = []
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for (down_block, attn_block) in zip(self.down_blocks, self.attn_blocks):
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x, unet_res = down_block(x)
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residuals.append(unet_res)
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if attn_block is not None:
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x = attn_block(x)
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x = self.conv(x) + x
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enc_out = self.to_logit(x)
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for (up_block, res) in zip(self.up_blocks, residuals[:-1][::-1]):
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x = up_block(x, res)
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dec_out = self.conv_out(x)
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return dec_out
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