spsr7 adjustments
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@ -528,16 +528,16 @@ class Spsr7(nn.Module):
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transform_count=self.transformation_counts, init_temp=init_temperature,
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add_scalable_noise_to_transforms=False, feed_transforms_into_multiplexer=True)
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self.sw1_out = nn.Sequential(ConvGnLelu(nf, nf, kernel_size=3, norm=False, activation=True),
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ConvGnLelu(nf, 3, kernel_size=1, norm=False, activation=False, bias=True))
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ConvGnLelu(nf, 3, kernel_size=1, norm=False, activation=False))
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self.sw2 = ConfigurableSwitchComputer(transformation_filters, multiplx_fn,
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pre_transform_block=None, transform_block=transform_fn,
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attention_norm=True,
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transform_count=self.transformation_counts, init_temp=init_temperature,
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add_scalable_noise_to_transforms=False, feed_transforms_into_multiplexer=True)
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self.feature_lr_conv = ConvGnLelu(nf, nf, kernel_size=3, norm=True, activation=False)
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self.feature_lr_conv = ConvGnLelu(nf, nf, kernel_size=3, norm=True, activation=False, bias=False)
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self.feature_lr_conv2 = ConvGnLelu(nf, nf, kernel_size=3, norm=False, activation=False, bias=False)
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self.sw2_out = nn.Sequential(ConvGnLelu(nf, nf, kernel_size=3, norm=False, activation=True),
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ConvGnLelu(nf, 3, kernel_size=1, norm=False, activation=False, bias=True))
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ConvGnLelu(nf, 3, kernel_size=1, norm=False, activation=False))
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# Grad branch. Note - groupnorm on this branch is REALLY bad. Avoid it like the plague.
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self.get_g_nopadding = ImageGradientNoPadding()
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@ -587,6 +587,7 @@ class Spsr7(nn.Module):
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x2 = x1
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x2, a2 = self.sw2(x2, True, identity=x1, att_in=(x2, ref_embedding))
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x2 = self.feature_lr_conv2(self.feature_lr_conv(x2))
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s2out = self.sw2_out(x2)
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x_grad = self.grad_conv(x_grad)
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