From c23f666dba2b484d521d2dc4be91cf9e09312647 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Fri, 21 Oct 2022 09:47:43 +0300
Subject: [PATCH] a more strict check for activation type and a more reasonable
 check for type of layer in hypernets

---
 modules/hypernetworks/hypernetwork.py | 12 +++++++++---
 1 file changed, 9 insertions(+), 3 deletions(-)

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index 7d617680f..84e7e350d 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -32,10 +32,16 @@ class HypernetworkModule(torch.nn.Module):
         linears = []
         for i in range(len(layer_structure) - 1):
             linears.append(torch.nn.Linear(int(dim * layer_structure[i]), int(dim * layer_structure[i+1])))
+
             if activation_func == "relu":
                 linears.append(torch.nn.ReLU())
-            if activation_func == "leakyrelu":
+            elif activation_func == "leakyrelu":
                 linears.append(torch.nn.LeakyReLU())
+            elif activation_func == 'linear' or activation_func is None:
+                pass
+            else:
+                raise RuntimeError(f'hypernetwork uses an unsupported activation function: {activation_func}')
+
             if add_layer_norm:
                 linears.append(torch.nn.LayerNorm(int(dim * layer_structure[i+1])))
 
@@ -46,7 +52,7 @@ class HypernetworkModule(torch.nn.Module):
             self.load_state_dict(state_dict)
         else:
             for layer in self.linear:
-                if not "ReLU" in layer.__str__():
+                if type(layer) == torch.nn.Linear:
                     layer.weight.data.normal_(mean=0.0, std=0.01)
                     layer.bias.data.zero_()
 
@@ -74,7 +80,7 @@ class HypernetworkModule(torch.nn.Module):
     def trainables(self):
         layer_structure = []
         for layer in self.linear:
-            if not "ReLU" in layer.__str__():
+            if type(layer) == torch.nn.Linear:
                 layer_structure += [layer.weight, layer.bias]
         return layer_structure
 
-- 
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