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import torch.nn as nn
import torch as th
from model.nn.residual import SkipConnection, ResidualBlock
from model.utils.nn_utils import Gaus2D, SharedObjectsToBatch, BatchToSharedObjects, Prioritize
from einops import rearrange, repeat, reduce
from typing import Tuple, Union, List
__author__ = "Manuel Traub"
class PriorityEncoder(nn.Module):
def __init__(self, num_objects, batch_size):
super(PriorityEncoder, self).__init__()
self.num_objects = num_objects
self.register_buffer("indices", repeat(th.arange(num_objects), 'a -> b a', b=batch_size), persistent=False)
self.index_factor = nn.Parameter(th.ones(1))
self.priority_factor = nn.Parameter(th.ones(1))
def forward(self, priority: th.Tensor) -> th.Tensor:
if priority is None:
return None
priority = priority * self.num_objects + th.randn_like(priority) * 0.1
priority = priority * self.priority_factor
priority = priority + self.indices * self.index_factor
return priority * 25
class GestaltPositionMerge(nn.Module):
def __init__(
self,
latent_size: Union[int, Tuple[int, int]],
num_objects: int,
batch_size: int
):
super(GestaltPositionMerge, self).__init__()
self.num_objects = num_objects
self.gaus2d = Gaus2D(size=latent_size)
self.to_batch = SharedObjectsToBatch(num_objects)
self.to_shared = BatchToSharedObjects(num_objects)
self.prioritize = Prioritize(num_objects)
self.priority_encoder = PriorityEncoder(num_objects, batch_size)
def forward(self, position, gestalt, priority):
position = rearrange(position, 'b (o c) -> (b o) c', o = self.num_objects)
gestalt = rearrange(gestalt, 'b (o c) -> (b o) c 1 1', o = self.num_objects)
priority = self.priority_encoder(priority)
position = self.gaus2d(position)
position = self.to_batch(self.prioritize(self.to_shared(position), priority))
return position * gestalt
class PatchUpscale(nn.Module):
def __init__(self, in_channels, out_channels, scale_factor = 4, alpha = 1):
super(PatchUpscale, self).__init__()
assert in_channels % out_channels == 0
self.skip = SkipConnection(in_channels, out_channels, scale_factor=scale_factor)
self.residual = nn.Sequential(
nn.ReLU(),
nn.Conv2d(
in_channels = in_channels,
out_channels = in_channels,
kernel_size = 3,
padding = 1
),
nn.ReLU(),
nn.ConvTranspose2d(
in_channels = in_channels,
out_channels = out_channels,
kernel_size = scale_factor,
stride = scale_factor,
),
)
self.alpha = nn.Parameter(th.zeros(1) + alpha)
def forward(self, input):
return self.skip(input) + self.alpha * self.residual(input)
class LociDecoder(nn.Module):
def __init__(
self,
latent_size: Union[int, Tuple[int, int]],
gestalt_size: int,
num_objects: int,
img_channels: int,
hidden_channels: int,
level1_channels: int,
num_layers: int,
batch_size: int
):
super(LociDecoder, self).__init__()
self.to_batch = SharedObjectsToBatch(num_objects)
self.to_shared = BatchToSharedObjects(num_objects)
self.level = 1
assert(level1_channels % img_channels == 0)
level1_factor = level1_channels // img_channels
print(f"Level1 channels: {level1_channels}")
self.merge = GestaltPositionMerge(
latent_size = latent_size,
num_objects = num_objects,
batch_size = batch_size
)
self.layer0 = nn.Sequential(
ResidualBlock(gestalt_size, hidden_channels, input_nonlinearity = False),
*[ResidualBlock(hidden_channels, hidden_channels) for _ in range(num_layers-1)],
)
self.to_mask_level0 = ResidualBlock(hidden_channels, hidden_channels)
self.to_mask_level1 = PatchUpscale(hidden_channels, 1)
self.to_mask_level2 = nn.Sequential(
ResidualBlock(hidden_channels, hidden_channels),
ResidualBlock(hidden_channels, hidden_channels),
PatchUpscale(hidden_channels, level1_factor, alpha = 1),
PatchUpscale(level1_factor, 1, alpha = 1)
)
self.to_object_level0 = ResidualBlock(hidden_channels, hidden_channels)
self.to_object_level1 = PatchUpscale(hidden_channels, img_channels)
self.to_object_level2 = nn.Sequential(
ResidualBlock(hidden_channels, hidden_channels),
ResidualBlock(hidden_channels, hidden_channels),
PatchUpscale(hidden_channels, level1_channels, alpha = 1),
PatchUpscale(level1_channels, img_channels, alpha = 1)
)
self.mask_alpha = nn.Parameter(th.zeros(1)+1e-16)
self.object_alpha = nn.Parameter(th.zeros(1)+1e-16)
def set_level(self, level):
self.level = level
def forward(self, position, gestalt, priority = None):
maps = self.layer0(self.merge(position, gestalt, priority))
mask0 = self.to_mask_level0(maps)
object0 = self.to_object_level0(maps)
mask = self.to_mask_level1(mask0)
object = self.to_object_level1(object0)
if self.level > 1:
mask = repeat(mask, 'b c h w -> b c (h h2) (w w2)', h2 = 4, w2 = 4)
object = repeat(object, 'b c h w -> b c (h h2) (w w2)', h2 = 4, w2 = 4)
mask = mask + self.to_mask_level2(mask0) * self.mask_alpha
object = object + self.to_object_level2(object0) * self.object_alpha
return self.to_shared(mask), self.to_shared(object)
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