initiaö commit

1 files changed, 59 insertions, 0 deletions

diff --git a/model/nn/percept_gate_controller.py b/model/nn/percept_gate_controller.py
new file mode 100644
index 0000000..a548c20
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+++ b/model/nn/percept_gate_controller.py
@@ -0,0 +1,59 @@
+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)