Add ConfigurableSwitchComputer

codes/models/archs/SwitchedResidualGenerator_arch.py

@@ -132,6 +132,83 @@ class SwitchComputer(nn.Module):
         self.switch.set_attention_temperature(temp)
 
 
+class ConfigurableSwitchComputer(nn.Module):
+    def __init__(self, multiplexer_net, transform_block, transform_count, init_temp=20,
+                 enable_negative_transforms=False, add_scalable_noise_to_transforms=False):
+        super(ConfigurableSwitchComputer, self).__init__()
+        self.enable_negative_transforms = enable_negative_transforms
+
+        tc = transform_count
+        if self.enable_negative_transforms:
+            tc = transform_count * 2
+        self.multiplexer = multiplexer_net(tc)
+
+        self.transforms = nn.ModuleList([transform_block() for _ in range(transform_count)])
+        self.add_noise = add_scalable_noise_to_transforms
+
+        # And the switch itself, including learned scalars
+        self.switch = BareConvSwitch(initial_temperature=init_temp)
+        self.scale = nn.Parameter(torch.ones(1))
+        self.bias = nn.Parameter(torch.zeros(1))
+
+    def forward(self, x, output_attention_weights=False):
+        if self.add_noise:
+            rand_feature = torch.randn_like(x)
+            xformed = [t.forward(x, rand_feature) for t in self.transforms]
+        else:
+            xformed = [t.forward(x) for t in self.transforms]
+        if self.enable_negative_transforms:
+            xformed.extend([-t for t in xformed])
+
+        m = self.multiplexer(x)
+        # Interpolate the multiplexer across the entire shape of the image.
+        m = F.interpolate(m, size=x.shape[2:], mode='nearest')
+
+        outputs, attention = self.switch(xformed, m, True)
+        outputs = outputs * self.scale + self.bias
+        if output_attention_weights:
+            return outputs, attention
+        else:
+            return outputs
+
+    def set_temperature(self, temp):
+        self.switch.set_attention_temperature(temp)
+
+
+class ResidualBasisMultiplexerBase(nn.Module):
+    def __init__(self, input_channels, base_filters, growth, reductions, processing_depth):
+        super(ResidualBasisMultiplexerBase, self).__init__()
+        self.filter_conv = ConvBnLelu(input_channels, base_filters)
+        self.reduction_blocks = nn.Sequential(OrderedDict([('block%i:' % (i,), HalvingProcessingBlock(base_filters * 2 ** i)) for i in range(reductions)]))
+        reduction_filters = base_filters * 2 ** reductions
+        self.processing_blocks, self.output_filter_count = create_sequential_growing_processing_block(reduction_filters, growth, processing_depth)
+
+    def forward(self, x):
+        x = self.filter_conv(x)
+        x = self.reduction_blocks(x)
+        x = self.processing_blocks(x)
+        return x
+
+
+class ResidualBasisMultiplexerLeaf(nn.Module):
+    def __init__(self, base, filters, multiplexer_channels, use_bn=False):
+        super(ResidualBasisMultiplexerLeaf, self).__init__()
+        assert(filters > multiplexer_channels)
+        gap = filters - multiplexer_channels
+        assert(gap % 4 == 0)
+        self.base = base
+        self.cbl1 = ConvBnLelu(filters, filters - (gap // 4), bn=use_bn)
+        self.cbl2 = ConvBnLelu(filters - (gap // 4), filters - (gap // 2), bn=use_bn)
+        self.cbl3 = ConvBnLelu(filters - (gap // 2), multiplexer_channels)
+
+    def forward(self, x):
+        x = self.base(x)
+        x = self.cbl1(x)
+        x = self.cbl2(x)
+        x = self.cbl3(x)
+        return x
+
+
 class ConfigurableSwitchedResidualGenerator(nn.Module):
     def __init__(self, switch_filters, switch_growths, switch_reductions, switch_processing_layers, trans_counts, trans_kernel_sizes,
                  trans_layers, trans_filters_mid, initial_temp=20, final_temperature_step=50000, heightened_temp_min=1,
@@ -196,3 +273,71 @@ class ConfigurableSwitchedResidualGenerator(nn.Module):
             val["switch_%i_specificity" % (i,)] = means[i]
             val["switch_%i_histogram" % (i,)] = hists[i]
         return val
+
+
+class ConfigurableSwitchedResidualGenerator2(nn.Module):
+    def __init__(self, switch_filters, switch_growths, switch_reductions, switch_processing_layers, trans_counts, trans_kernel_sizes,
+                 trans_layers, trans_filters_mid, initial_temp=20, final_temperature_step=50000, heightened_temp_min=1,
+                 heightened_final_step=50000, upsample_factor=1, enable_negative_transforms=False,
+                 add_scalable_noise_to_transforms=False):
+        super(ConfigurableSwitchedResidualGenerator2, self).__init__()
+        switches = []
+        multiplexer_base = ResidualBasisMultiplexerBase(3, switch_filters[0], switch_growths[0], switch_reductions[0], switch_processing_layers[0])
+        for trans_count, kernel, layers, mid_filters in zip(trans_counts, trans_kernel_sizes, trans_layers, trans_filters_mid):
+            leaf_fn = functools.partial(ResidualBasisMultiplexerLeaf, multiplexer_base, multiplexer_base.output_filter_count)
+            switches.append(ConfigurableSwitchComputer(leaf_fn, functools.partial(ResidualBranch, 3, mid_filters, 3, 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))
+        initialize_weights(switches, 1)
+        # Initialize the transforms with a lesser weight, since they are repeatedly added on to the resultant image.
+        initialize_weights([s.transforms for s in switches], .2 / len(switches))
+        self.switches = nn.ModuleList(switches)
+        self.transformation_counts = trans_counts
+        self.init_temperature = initial_temp
+        self.final_temperature_step = final_temperature_step
+        self.heightened_temp_min = heightened_temp_min
+        self.heightened_final_step = heightened_final_step
+        self.attentions = None
+        self.upsample_factor = upsample_factor
+
+    def forward(self, x):
+        # This network is entirely a "repair" network and operates on full-resolution images. Upsample first if that
+        # is called for, then repair.
+        if self.upsample_factor > 1:
+            x = F.interpolate(x, scale_factor=self.upsample_factor, mode="nearest")
+
+        self.attentions = []
+        for i, sw in enumerate(self.switches):
+            sw_out, att = sw.forward(x, True)
+            x = x + sw_out
+            self.attentions.append(att)
+        return x,
+
+    def set_temperature(self, temp):
+        [sw.set_temperature(temp) for sw in self.switches]
+
+    def update_for_step(self, step, experiments_path='.'):
+        if self.attentions:
+            temp = max(1, int(self.init_temperature * (self.final_temperature_step - step) / self.final_temperature_step))
+            if temp == 1 and self.heightened_final_step and self.heightened_final_step != 1:
+                # Once the temperature passes (1) it enters an inverted curve to match the linear curve from above.
+                # without this, the attention specificity "spikes" incredibly fast in the last few iterations.
+                h_steps_total = self.heightened_final_step - self.final_temperature_step
+                h_steps_current = min(step - self.final_temperature_step, h_steps_total)
+                # The "gap" will represent the steps that need to be traveled as a linear function.
+                h_gap = 1 / self.heightened_temp_min
+                temp = h_gap * h_steps_current / h_steps_total
+                # Invert temperature to represent reality on this side of the curve
+                temp = 1 / temp
+            self.set_temperature(temp)
+            if step % 50 == 0:
+                [save_attention_to_image(experiments_path, self.attentions[i], self.transformation_counts[i], step, "a%i" % (i+1,)) for i in range(len(self.switches))]
+
+    def get_debug_values(self, step):
+        temp = self.switches[0].switch.temperature
+        mean_hists = [compute_attention_specificity(att, 2) for att in self.attentions]
+        means = [i[0] for i in mean_hists]
+        hists = [i[1].clone().detach().cpu().flatten() for i in mean_hists]
+        val = {"switch_temperature": temp}
+        for i in range(len(means)):
+            val["switch_%i_specificity" % (i,)] = means[i]
+            val["switch_%i_histogram" % (i,)] = hists[i]
+        return val

codes/models/networks.py

@@ -71,6 +71,15 @@ def define_G(opt, net_key='network_G'):
                                                                       initial_temp=opt_net['temperature'], final_temperature_step=opt_net['temperature_final_step'],
                                                                       heightened_temp_min=opt_net['heightened_temp_min'], heightened_final_step=opt_net['heightened_final_step'],
                                                                       upsample_factor=scale, add_scalable_noise_to_transforms=opt_net['add_noise'])
+    elif which_model == "ConfigurableSwitchedResidualGenerator2":
+        netG = SwitchedGen_arch.ConfigurableSwitchedResidualGenerator2(switch_filters=opt_net['switch_filters'], switch_growths=opt_net['switch_growths'],
+                                                                      switch_reductions=opt_net['switch_reductions'],
+                                                                      switch_processing_layers=opt_net['switch_processing_layers'], trans_counts=opt_net['trans_counts'],
+                                                                      trans_kernel_sizes=opt_net['trans_kernel_sizes'], trans_layers=opt_net['trans_layers'],
+                                                                      trans_filters_mid=opt_net['trans_filters_mid'],
+                                                                      initial_temp=opt_net['temperature'], final_temperature_step=opt_net['temperature_final_step'],
+                                                                      heightened_temp_min=opt_net['heightened_temp_min'], heightened_final_step=opt_net['heightened_final_step'],
+                                                                      upsample_factor=scale, add_scalable_noise_to_transforms=opt_net['add_noise'])
 
     # image corruption
     elif which_model == 'HighToLowResNet':