Experiment: revert initialization changes
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@ -116,7 +116,7 @@ def create_sequential_growing_processing_block(filters_init, filter_growth, num_
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convs = []
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current_filters = filters_init
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for i in range(num_convs):
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convs.append(ConvBnLelu(current_filters, current_filters + filter_growth, bn=True, bias=False))
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convs.append(ConvBnRelu(current_filters, current_filters + filter_growth, bn=True, bias=False))
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current_filters += filter_growth
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return nn.Sequential(*convs), current_filters
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@ -222,15 +222,15 @@ class ConfigurableSwitchComputer(nn.Module):
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class ConvBasisMultiplexer(nn.Module):
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def __init__(self, input_channels, base_filters, growth, reductions, processing_depth, multiplexer_channels, use_bn=True):
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super(ConvBasisMultiplexer, self).__init__()
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self.filter_conv = ConvBnLelu(input_channels, base_filters, bias=True)
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self.filter_conv = ConvBnRelu(input_channels, base_filters, bias=True)
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self.reduction_blocks = nn.Sequential(OrderedDict([('block%i:' % (i,), HalvingProcessingBlock(base_filters * 2 ** i)) for i in range(reductions)]))
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reduction_filters = base_filters * 2 ** reductions
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self.processing_blocks, self.output_filter_count = create_sequential_growing_processing_block(reduction_filters, growth, processing_depth)
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gap = self.output_filter_count - multiplexer_channels
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self.cbl1 = ConvBnLelu(self.output_filter_count, self.output_filter_count - (gap // 2), bn=use_bn, bias=False)
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self.cbl2 = ConvBnLelu(self.output_filter_count - (gap // 2), self.output_filter_count - (3 * gap // 4), bn=use_bn, bias=False)
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self.cbl3 = ConvBnLelu(self.output_filter_count - (3 * gap // 4), multiplexer_channels, bias=True)
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self.cbl1 = ConvBnRelu(self.output_filter_count, self.output_filter_count - (gap // 2), bn=use_bn, bias=False)
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self.cbl2 = ConvBnRelu(self.output_filter_count - (gap // 2), self.output_filter_count - (3 * gap // 4), bn=use_bn, bias=False)
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self.cbl3 = ConvBnRelu(self.output_filter_count - (3 * gap // 4), multiplexer_channels, bias=True)
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def forward(self, x):
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x = self.filter_conv(x)
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@ -351,14 +351,17 @@ class ConfigurableSwitchedResidualGenerator2(nn.Module):
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switches = []
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post_switch_proc = []
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self.initial_conv = ConvBnLelu(3, transformation_filters, bn=False)
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self.proc_conv = ConvBnLelu(transformation_filters, transformation_filters, bn=False, bias=False)
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self.proc_conv = ConvBnLelu(transformation_filters, transformation_filters, bn=False)
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self.final_conv = ConvBnLelu(transformation_filters, 3, bn=False, lelu=False)
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for filters, growth, sw_reduce, sw_proc, trans_count, kernel, layers in zip(switch_filters, switch_growths, switch_reductions, switch_processing_layers, trans_counts, trans_kernel_sizes, trans_layers):
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multiplx_fn = functools.partial(ConvBasisMultiplexer, transformation_filters, filters, growth, sw_reduce, sw_proc, trans_count)
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switches.append(ConfigurableSwitchComputer(multiplx_fn, functools.partial(MultiConvBlock, transformation_filters, transformation_filters, transformation_filters, kernel_size=kernel, depth=layers), trans_count, initial_temp, enable_negative_transforms=enable_negative_transforms, add_scalable_noise_to_transforms=add_scalable_noise_to_transforms))
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post_switch_proc.append(ConvBnLelu(transformation_filters, transformation_filters, bn=False))
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initialize_weights(switches, 1)
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# Initialize the transforms with a lesser weight, since they are repeatedly added on to the resultant image.
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initialize_weights([s.transforms for s in switches], .2 / len(switches))
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self.switches = nn.ModuleList(switches)
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initialize_weights([p for p in post_switch_proc], .01)
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self.post_switch_convs = nn.ModuleList(post_switch_proc)
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self.transformation_counts = trans_counts
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self.init_temperature = initial_temp
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@ -384,7 +387,7 @@ class ConfigurableSwitchedResidualGenerator2(nn.Module):
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x = self.proc_conv(x)
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x = self.final_conv(x)
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return x / 13,
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return x,
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def set_temperature(self, temp):
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[sw.set_temperature(temp) for sw in self.switches]
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