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import torch.nn as nn
import torch as th
from model.nn.residual import ResidualBlock, SkipConnection, LinearResidual
from model.nn.encoder import PatchDownConv
from model.nn.encoder import AggressiveConvToGestalt
from model.nn.decoder import PatchUpscale
from model.utils.nn_utils import LambdaModule, Binarize
from einops import rearrange, repeat, reduce
from typing import Tuple
__author__ = "Manuel Traub"
class BackgroundEnhancer(nn.Module):
def __init__(
self,
input_size: Tuple[int, int],
img_channels: int,
level1_channels,
latent_channels,
gestalt_size,
batch_size,
depth
):
super(BackgroundEnhancer, self).__init__()
latent_size = [input_size[0] // 16, input_size[1] // 16]
self.input_size = input_size
self.register_buffer('init', th.zeros(1).long())
self.alpha = nn.Parameter(th.zeros(1)+1e-16)
self.level = 1
self.down_level2 = nn.Sequential(
PatchDownConv(img_channels*2+2, level1_channels, alpha = 1e-16),
*[ResidualBlock(level1_channels, level1_channels, alpha_residual = True) for i in range(depth)]
)
self.down_level1 = nn.Sequential(
PatchDownConv(level1_channels, latent_channels, alpha = 1),
*[ResidualBlock(latent_channels, latent_channels, alpha_residual = False) for i in range(depth)]
)
self.down_level0 = nn.Sequential(
*[ResidualBlock(latent_channels, latent_channels) for i in range(depth)],
AggressiveConvToGestalt(latent_channels, gestalt_size, latent_size),
LambdaModule(lambda x: rearrange(x, 'b c 1 1 -> b c')),
Binarize(),
)
self.bias = nn.Parameter(th.zeros((1, gestalt_size, *latent_size)))
self.to_grid = nn.Sequential(
LinearResidual(gestalt_size, gestalt_size, input_relu = False),
LambdaModule(lambda x: rearrange(x, 'b c -> b c 1 1')),
LambdaModule(lambda x: x + self.bias),
*[ResidualBlock(gestalt_size, gestalt_size) for i in range(depth)],
)
self.up_level0 = nn.Sequential(
ResidualBlock(gestalt_size, latent_channels),
*[ResidualBlock(latent_channels, latent_channels) for i in range(depth)],
)
self.up_level1 = nn.Sequential(
*[ResidualBlock(latent_channels, latent_channels, alpha_residual = False) for i in range(depth)],
PatchUpscale(latent_channels, level1_channels, alpha = 1),
)
self.up_level2 = nn.Sequential(
*[ResidualBlock(level1_channels, level1_channels, alpha_residual = True) for i in range(depth)],
PatchUpscale(level1_channels, img_channels, alpha = 1e-16),
)
self.to_channels = nn.ModuleList([
SkipConnection(img_channels*2+2, latent_channels),
SkipConnection(img_channels*2+2, level1_channels),
SkipConnection(img_channels*2+2, img_channels*2+2),
])
self.to_img = nn.ModuleList([
SkipConnection(latent_channels, img_channels),
SkipConnection(level1_channels, img_channels),
SkipConnection(img_channels, img_channels),
])
self.mask = nn.Parameter(th.ones(1, 1, *input_size) * 10)
self.object = nn.Parameter(th.ones(1, img_channels, *input_size))
self.register_buffer('latent', th.zeros((batch_size, gestalt_size)), persistent=False)
def get_init(self):
return self.init.item()
def step_init(self):
self.init = self.init + 1
def detach(self):
self.latent = self.latent.detach()
def reset_state(self):
self.latent = th.zeros_like(self.latent)
def set_level(self, level):
self.level = level
def encoder(self, input):
latent = self.to_channels[self.level](input)
if self.level >= 2:
latent = self.down_level2(latent)
if self.level >= 1:
latent = self.down_level1(latent)
return self.down_level0(latent)
def get_last_latent_gird(self):
return self.to_grid(self.latent) * self.alpha
def decoder(self, latent, input):
grid = self.to_grid(latent)
latent = self.up_level0(grid)
if self.level >= 1:
latent = self.up_level1(latent)
if self.level >= 2:
latent = self.up_level2(latent)
object = reduce(self.object, '1 c (h h2) (w w2) -> 1 c h w', 'mean', h = input.shape[2], w = input.shape[3])
object = repeat(object, '1 c h w -> b c h w', b = input.shape[0])
return th.sigmoid(object + self.to_img[self.level](latent)), grid
def forward(self, input: th.Tensor, error: th.Tensor = None, mask: th.Tensor = None, only_mask: bool = False):
if only_mask:
mask = reduce(self.mask, '1 1 (h h2) (w w2) -> 1 1 h w', 'mean', h = input.shape[2], w = input.shape[3])
mask = repeat(mask, '1 1 h w -> b 1 h w', b = input.shape[0]) * 0.1
return mask
last_bg = self.decoder(self.latent, input)[0]
bg_error = th.sqrt(reduce((input - last_bg)**2, 'b c h w -> b 1 h w', 'mean')).detach()
bg_mask = (bg_error < th.mean(bg_error) + th.std(bg_error)).float().detach()
if error is None or self.get_init() < 2:
error = bg_error
if mask is None or self.get_init() < 2:
mask = bg_mask
self.latent = self.encoder(th.cat((input, last_bg, error, mask), dim=1))
mask = reduce(self.mask, '1 1 (h h2) (w w2) -> 1 1 h w', 'mean', h = input.shape[2], w = input.shape[3])
mask = repeat(mask, '1 1 h w -> b 1 h w', b = input.shape[0]) * 0.1
background, grid = self.decoder(self.latent, input)
if self.get_init() < 1:
return mask, background
if self.get_init() < 2:
return mask, th.zeros_like(background), th.zeros_like(grid), background
return mask, background, grid * self.alpha, background
|
