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import torch.nn as nn
import torch as th
from model.utils.nn_utils import LambdaModule
from einops import rearrange, repeat, reduce
from model.nn.eprop_gate_l0rd import ReTanh
class PerceptGateController(nn.Module):
def __init__(
self,
num_inputs: int,
num_hidden: list,
bias: bool,
num_objects: int,
gate_noise_level: float = 0.1,
reg_lambda: float = 0.000005
):
super(PerceptGateController, 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.layers = nn.Sequential(
nn.Linear(num_inputs, num_hidden[0], bias = bias),
nn.Tanh(),
nn.Linear(num_hidden[0], num_hidden[1], bias = bias),
nn.Tanh(),
nn.Linear(num_hidden[1], 2, bias = bias)
)
self.output_function = ReTanh(reg_lambda)
self.register_buffer("noise", th.tensor(gate_noise_level), persistent=False)
self.init_weights()
def init_weights(self):
for layer in self.layers:
if isinstance(layer, nn.Linear):
nn.init.xavier_uniform(layer.weight)
layer.bias.data.fill_(3.00)
def forward(self, position_cur, gestalt_cur, priority_cur, slots_occlusionfactor_cur, position_last, gestalt_last, priority_last, slots_occlusionfactor_last, position_last2, evaluate=False):
position_cur = self.to_batch(position_cur)
gestalt_cur = self.to_batch(gestalt_cur)
priority_cur = self.to_batch(priority_cur)
position_last = self.to_batch(position_last)
gestalt_last = self.to_batch(gestalt_last)
priority_last = self.to_batch(priority_last)
slots_occlusionfactor_cur = self.to_batch(slots_occlusionfactor_cur).detach()
slots_occlusionfactor_last = self.to_batch(slots_occlusionfactor_last).detach()
position_last2 = self.to_batch(position_last2).detach()
input = th.cat((position_cur, gestalt_cur, priority_cur, slots_occlusionfactor_cur, position_last, gestalt_last, priority_last, slots_occlusionfactor_last, position_last2), dim=1)
output = self.layers(input)
if evaluate:
output = self.output_function(output)
else:
noise = th.normal(mean=0, std=self.noise, size=output.shape, device=output.device)
output = self.output_function(output + noise)
return self.to_shared(output)
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