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Fix off-by-one
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@ -42,7 +42,7 @@ class HypernetworkModule(torch.nn.Module):
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linears.append(torch.nn.Linear(int(dim * layer_structure[i]), int(dim * layer_structure[i+1])))
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# Add an activation func except last layer
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if activation_func == "linear" or activation_func is None or i >= len(layer_structure) - 3:
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if activation_func == "linear" or activation_func is None or i >= len(layer_structure) - 2:
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pass
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elif activation_func in self.activation_dict:
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linears.append(self.activation_dict[activation_func]())
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@ -54,7 +54,7 @@ class HypernetworkModule(torch.nn.Module):
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linears.append(torch.nn.LayerNorm(int(dim * layer_structure[i+1])))
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# Add dropout except last layer
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if use_dropout and i < len(layer_structure) - 3:
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if use_dropout and i < len(layer_structure) - 2:
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linears.append(torch.nn.Dropout(p=0.3))
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self.linear = torch.nn.Sequential(*linears)
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