forked from mrq/DL-Art-School
Set post_transform_block to None where applicable
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@ -99,8 +99,7 @@ class ConfigurableSwitchComputer(nn.Module):
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# And the switch itself, including learned scalars
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self.switch = BareConvSwitch(initial_temperature=init_temp, attention_norm=AttentionNorm(transform_count, accumulator_size=anorm_multiplier * transform_count) if attention_norm else None)
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self.switch_scale = nn.Parameter(torch.full((1,), float(1)))
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if post_transform_block is not None:
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self.post_transform_block = post_transform_block
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self.post_transform_block = post_transform_block
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if post_switch_conv:
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self.post_switch_conv = ConvBnLelu(base_filters, base_filters, norm=False, bias=True)
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# The post_switch_conv gets a low scale initially. The network can decide to magnify it (or not)
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