Add SRG4

Back to the idea that maybe what we need is a hybrid
approach between pure switches and RDB.

.idea/misc.xml

@@ -3,4 +3,5 @@
   <component name="JavaScriptSettings">
     <option name="languageLevel" value="ES6" />
   </component>
+  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.7 (torch-1.4-venv) (2)" project-jdk-type="Python SDK" />
 </project>

codes/models/archs/SwitchedResidualGenerator_arch.py

@@ -5,7 +5,7 @@ import torch.nn.functional as F
 import functools
 from collections import OrderedDict
 from models.archs.arch_util import ConvBnLelu, ConvGnSilu, ExpansionBlock
-from models.archs.RRDBNet_arch import ResidualDenseBlock_5C
+from models.archs.RRDBNet_arch import ResidualDenseBlock_5C, RRDB
 from models.archs.spinenet_arch import SpineNet
 from switched_conv_util import save_attention_to_image
 
@@ -117,7 +117,10 @@ class ConfigurableSwitchComputer(nn.Module):
         tc = transform_count
         self.multiplexer = multiplexer_net(tc)
 
-        self.pre_transform = pre_transform_block()
+        if pre_transform_block:
+            self.pre_transform = pre_transform_block()
+        else:
+            self.pre_transform = None
         self.transforms = nn.ModuleList([transform_block() for _ in range(transform_count)])
         self.add_noise = add_scalable_noise_to_transforms
         self.noise_scale = nn.Parameter(torch.full((1,), float(1e-3)))
@@ -237,6 +240,101 @@ class ConfigurableSwitchedResidualGenerator2(nn.Module):
             val["switch_%i_histogram" % (i,)] = hists[i]
         return val
 
+
+# Equivalent to SRG2 - Uses RDB blocks in between two switches.
+class ConfigurableSwitchedResidualGenerator4(nn.Module):
+    def __init__(self, switch_filters, switch_reductions, switch_processing_layers, trans_counts, trans_kernel_sizes,
+                 trans_layers, transformation_filters, attention_norm, initial_temp=20, final_temperature_step=50000, heightened_temp_min=1,
+                 heightened_final_step=50000, upsample_factor=1,
+                 add_scalable_noise_to_transforms=False):
+        super(ConfigurableSwitchedResidualGenerator4, self).__init__()
+        self.initial_conv = ConvBnLelu(3, transformation_filters, norm=False, activation=False, bias=True)
+        self.upconv1 = ConvBnLelu(transformation_filters, transformation_filters, norm=False, bias=True)
+        self.upconv2 = ConvBnLelu(transformation_filters, transformation_filters, norm=False, bias=True)
+        self.hr_conv = ConvBnLelu(transformation_filters, transformation_filters, norm=False, bias=True)
+
+        multiplx_fn = functools.partial(ConvBasisMultiplexer, transformation_filters, switch_filters, switch_reductions,
+                                        switch_processing_layers, trans_counts)
+        transform_fn = functools.partial(MultiConvBlock, transformation_filters, int(transformation_filters * 1.5),
+                                         transformation_filters, kernel_size=trans_kernel_sizes, depth=trans_layers,
+                                         weight_init_factor=.1)
+        self.rdb1 = RRDB(transformation_filters)
+        self.sw1 = ConfigurableSwitchComputer(transformation_filters, multiplx_fn,
+                                                   pre_transform_block=None, transform_block=transform_fn,
+                                                   attention_norm=attention_norm,
+                                                   transform_count=trans_counts, init_temp=initial_temp,
+                                                   add_scalable_noise_to_transforms=add_scalable_noise_to_transforms)
+        self.rdb2 = RRDB(transformation_filters)
+        self.sw1 = ConfigurableSwitchComputer(transformation_filters, multiplx_fn,
+                                                   pre_transform_block=None, transform_block=transform_fn,
+                                                   attention_norm=attention_norm,
+                                                   transform_count=trans_counts, init_temp=initial_temp,
+                                                   add_scalable_noise_to_transforms=add_scalable_noise_to_transforms)
+        self.rdb3 = RRDB(transformation_filters)
+
+        self.final_conv = ConvBnLelu(transformation_filters, 3, norm=False, activation=False, bias=True)
+        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
+        assert self.upsample_factor == 2 or self.upsample_factor == 4
+
+    def forward(self, x):
+        # This is a common bug when evaluating SRG2 generators. It needs to be configured properly in eval mode. Just fail.
+        if not self.train:
+            assert self.switches[0].switch.temperature == 1
+
+        x = self.initial_conv(x)
+
+        x = self.rdb1(x)
+        x = self.sw1(x, True)
+        x = self.rdb2(x)
+        x = self.sw2(x, True)
+        x = self.rdb3(x)
+
+        x = self.upconv1(F.interpolate(x, scale_factor=2, mode="nearest"))
+        if self.upsample_factor > 2:
+            x = F.interpolate(x, scale_factor=2, mode="nearest")
+        x = self.upconv2(x)
+        x = self.final_conv(self.hr_conv(x))
+        return x, 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,
+                1 + self.init_temperature * (self.final_temperature_step - step) / self.final_temperature_step)
+            if temp == 1 and self.heightened_final_step and step > self.final_temperature_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, step, "a%i" % (i+1,), l_mult=10) for i in  range(len(self.attentions))]
+
+    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
+
 class Interpolate(nn.Module):
     def __init__(self, factor):
         super(Interpolate, self).__init__()

codes/models/networks.py

@@ -79,6 +79,15 @@ 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 == "ConfigurableSwitchedResidualGenerator4":
+        netG = SwitchedGen_arch.ConfigurableSwitchedResidualGenerator4(switch_filters=opt_net['switch_filters'],
+                                                                      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'],
+                                                                      transformation_filters=opt_net['transformation_filters'], attention_norm=opt_net['attention_norm'],
+                                                                      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 == "ProgressiveSRG2":
         netG = psrg.GrowingSRGBase(progressive_step_schedule=opt_net['schedule'], switch_reductions=opt_net['reductions'],
                                    growth_fade_in_steps=opt_net['fade_in_steps'], switch_filters=opt_net['switch_filters'],