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import torch as th
from torch import nn
from model.utils.nn_utils import SharedObjectsToBatch, LambdaModule
from einops import rearrange, repeat, reduce
__author__ = "Manuel Traub"
class PositionLoss(nn.Module):
def __init__(self, num_objects: int):
super(PositionLoss, self).__init__()
self.to_batch = LambdaModule(lambda x: rearrange(x, 'b (o c) -> (b o) c', o = num_objects))
def forward(self, position, position_last, slot_mask):
slot_mask = rearrange(slot_mask, 'b o -> (b o) 1 1 1')
position = self.to_batch(position)
position_last = self.to_batch(position_last).detach()
return th.mean(slot_mask * (position - position_last)**2)
class ObjectModulator(nn.Module):
def __init__(self, num_objects: int):
super(ObjectModulator, self).__init__()
self.to_batch = LambdaModule(lambda x: rearrange(x, 'b (o c) -> (b o) c', o = num_objects))
self.to_shared = LambdaModule(lambda x: rearrange(x, '(b o) c -> b (o c)', o = num_objects))
self.position = None
self.gestalt = None
def reset_state(self):
self.position = None
self.gestalt = None
def forward(self, position: th.Tensor, gestalt: th.Tensor, slot_mask: th.Tensor):
position = self.to_batch(position)
gestalt = self.to_batch(gestalt)
slot_mask = self.to_batch(slot_mask)
if self.position is None or self.gestalt is None:
self.position = position.detach()
self.gestalt = gestalt.detach()
return self.to_shared(position), self.to_shared(gestalt)
_position = slot_mask * position + (1 - slot_mask) * self.position
position = th.cat((position[:,:-1], _position[:,-1:]), dim=1) # keep the position of the objects fixed
gestalt = slot_mask * gestalt + (1 - slot_mask) * self.gestalt
self.gestalt = gestalt.detach()
self.position = position.detach()
return self.to_shared(position), self.to_shared(gestalt)
class MoveToCenter(nn.Module):
def __init__(self, num_objects: int):
super(MoveToCenter, self).__init__()
self.to_batch2d = SharedObjectsToBatch(num_objects)
self.to_batch = LambdaModule(lambda x: rearrange(x, 'b (o c) -> (b o) c', o = num_objects))
def forward(self, input: th.Tensor, position: th.Tensor):
input = self.to_batch2d(input) # b (o c) h w -> (b o) c h w
position = self.to_batch(position).detach()
position = th.stack((position[:,1], position[:,0]), dim=1)
theta = th.tensor([1, 0, 0, 1], dtype=th.float, device=input.device).view(1,2,2)
theta = repeat(theta, '1 a b -> n a b', n=input.shape[0])
position = rearrange(position, 'b c -> b c 1')
theta = th.cat((theta, position), dim=2)
grid = nn.functional.affine_grid(theta, input.shape, align_corners=False)
output = nn.functional.grid_sample(input, grid, align_corners=False)
return output
class TranslationInvariantObjectLoss(nn.Module):
def __init__(self, num_objects: int):
super(TranslationInvariantObjectLoss, self).__init__()
self.move_to_center = MoveToCenter(num_objects)
self.to_batch = SharedObjectsToBatch(num_objects)
def forward(
self,
slot_mask: th.Tensor,
object1: th.Tensor,
position1: th.Tensor,
object2: th.Tensor,
position2: th.Tensor,
):
slot_mask = rearrange(slot_mask, 'b o -> (b o) 1 1 1')
object1 = self.move_to_center(th.sigmoid(object1 - 2.5), position1)
object2 = self.move_to_center(th.sigmoid(object2 - 2.5), position2)
return th.mean(slot_mask * (object1 - object2)**2)
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