Restore SRG2

Going to try to figure out where SRG lost competitiveness to RRDB..

codes/models/archs/SwitchedResidualGenerator_arch.py

@@ -614,7 +614,95 @@ class SwitchModelBase(nn.Module):
         return val
 
 
-if __name__ == '__main__':
-    tbs = TheBigSwitch(3, 64)
-    x = torch.randn(4,3,64,64)
-    b = tbs(x)
+class ConfigurableSwitchedResidualGenerator2(nn.Module):
+    def __init__(self, switch_depth, 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(ConfigurableSwitchedResidualGenerator2, self).__init__()
+        switches = []
+        self.initial_conv = ConvBnLelu(3, transformation_filters, kernel_size=7, 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)
+        self.final_conv = ConvBnLelu(transformation_filters, 3, norm=False, activation=False, bias=True)
+        for _ in range(switch_depth):
+            multiplx_fn = functools.partial(ConvBasisMultiplexer, transformation_filters, switch_filters, switch_reductions, switch_processing_layers, trans_counts)
+            pretransform_fn = functools.partial(ConvBnLelu, transformation_filters, transformation_filters, norm=False, bias=False, weight_init_factor=.1)
+            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)
+            switches.append(ConfigurableSwitchComputer(transformation_filters, multiplx_fn,
+                                                       pre_transform_block=pretransform_fn, 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.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
+        self.lr = None
+        assert self.upsample_factor == 2 or self.upsample_factor == 4
+
+    def forward(self, x):
+        self.lr = x.detach().cpu()
+
+        # 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)
+
+        self.attentions = []
+        for i, sw in enumerate(self.switches):
+            x, att = checkpoint(sw, x)
+            self.attentions.append(att)
+
+        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
+
+    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 % 100 == 0:
+                output_path = os.path.join(experiments_path, "attention_maps")
+                prefix = "amap_%i_a%i_%%i.png"
+                [save_attention_to_image_rgb(output_path, self.attentions[i], self.attentions[i].shape[3], prefix % (step, i), step,
+                                             output_mag=False) for i in range(len(self.attentions))]
+                if self.lr is not None:
+                    torchvision.utils.save_image(self.lr[:, :3], os.path.join(experiments_path, "attention_maps",
+                                                                              "amap_%i_base_image.png" % (step,)))
+
+    def get_debug_values(self, step, net_name):
+        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