Add network architecture built for teco

codes/models/archs/StructuredSwitchedGenerator.py

@@ -372,3 +372,56 @@ class StackedSwitchGenerator5Layer(SwitchModelBase):
             self.attentions = [a1, a3, a3, a4, a5]
         return x_out,
 
+
+class StackedSwitchGenerator2xTeco(SwitchModelBase):
+    def __init__(self, nf, xforms=8, init_temperature=10):
+        super(StackedSwitchGenerator2xTeco, self).__init__(init_temperature, 10000)
+        self.nf = nf
+
+        # processing the input embedding
+        self.reference_embedding = ReferenceImageBranch(nf)
+
+        # Feature branch
+        self.model_fea_conv = ConvGnLelu(3, nf, kernel_size=7, norm=False, activation=False, bias=True)
+        self.model_recurrent_conv = ConvGnLelu(3, nf, kernel_size=3, stride=2, norm=False, activation=False, bias=True)
+        self.model_fea_recurrent_combine = ConvGnLelu(nf*2, nf, 1, activation=False, norm=False, bias=False)
+        self.sw1 = SwitchWithReference(nf, xforms, init_temperature, has_ref=False)
+        self.sw2 = SwitchWithReference(nf, xforms // 2, init_temperature, has_ref=False)
+        self.sw3 = SwitchWithReference(nf, xforms // 2, init_temperature, has_ref=False)
+        self.sw4 = SwitchWithReference(nf, xforms // 2, init_temperature, has_ref=False)
+        self.sw5 = SwitchWithReference(nf, xforms, init_temperature, has_ref=False)
+        self.switches = [self.sw1.switch, self.sw2.switch, self.sw3.switch, self.sw4.switch, self.sw5.switch]
+
+        self.final_lr_conv = ConvGnLelu(nf, nf, kernel_size=3, norm=False, activation=True, bias=True)
+        self.upsample = UpconvBlock(nf, nf // 2, block=ConvGnLelu, norm=False, activation=True, bias=True)
+        self.final_hr_conv1 = ConvGnLelu(nf // 2, nf // 2, kernel_size=3, norm=False, activation=False, bias=True)
+        self.final_hr_conv2 = ConvGnLelu(nf // 2, 3, kernel_size=3, norm=False, activation=False, bias=False)
+
+    def forward(self, x, recurrent, ref, ref_center, save_attentions=True):
+        # The attention_maps debugger outputs <x>. Save that here.
+        self.lr = x.detach().cpu()
+
+        # If we're not saving attention, we also shouldn't be updating the attention norm. This is because the attention
+        # norm should only be getting updates with new data, not recurrent generator sampling.
+        for sw in self.switches:
+            sw.set_update_attention_norm(save_attentions)
+
+        ref_code = checkpoint(self.reference_embedding, ref, ref_center)
+        ref_embedding = ref_code.view(-1, ref_code.shape[1], 1, 1).repeat(1, 1, x.shape[2] // 8, x.shape[3] // 8)
+
+        x = self.model_fea_conv(x)
+        rec = self.model_recurrent_conv(recurrent)
+        x = self.model_fea_recurrent_combine(torch.cat([x, rec], dim=1))
+        x1, a1 = checkpoint(self.sw1, x, ref_embedding)
+        x2, a2 = checkpoint(self.sw2, x1, ref_embedding)
+        x3, a3 = checkpoint(self.sw3, x2, ref_embedding)
+        x4, a4 = checkpoint(self.sw4, x3, ref_embedding)
+        x5, a5 = checkpoint(self.sw5, x4, ref_embedding)
+        x_out = checkpoint(self.final_lr_conv, x5)
+        x_out = checkpoint(self.upsample, x_out)
+        x_out = checkpoint(self.final_hr_conv2, x_out)
+
+        if save_attentions:
+            self.attentions = [a1, a3, a3, a4, a5]
+        return x_out,
+

codes/models/networks.py

@@ -98,6 +98,8 @@ def define_G(opt, net_key='network_G', scale=None):
         xforms = opt_net['num_transforms'] if 'num_transforms' in opt_net.keys() else 8
         netG = ssg.SSGDeep(in_nc=3, out_nc=3, nf=opt_net['nf'], xforms=xforms, upscale=opt_net['scale'],
                                  init_temperature=opt_net['temperature'] if 'temperature' in opt_net.keys() else 10)
+    elif which_model == 'ssg_teco':
+        netG = ssg.StackedSwitchGenerator2xTeco(nf=opt_net['nf'], xforms=opt_net['num_transforms'], init_temperature=opt_net['temperature'] if 'temperature' in opt_net.keys() else 10)
     elif which_model == "flownet2":
         from models.flownet2.models import FlowNet2
         ld = torch.load(opt_net['load_path'])