LR switched SPSR arch

This variant doesn't do conv processing at HR, which should save
a ton of memory in inference. Lets see how it works.

codes/models/archs/SPSR_arch.py

@@ -546,3 +546,129 @@ class SwitchedSpsr(nn.Module):
             val["switch_%i_specificity" % (i,)] = means[i]
             val["switch_%i_histogram" % (i,)] = hists[i]
         return val
+
+
+
+
+class SwitchedSpsrLr(nn.Module):
+    def __init__(self, in_nc, out_nc, nf, upscale=4):
+        super(SwitchedSpsrLr, self).__init__()
+        n_upscale = int(math.log(upscale, 2))
+
+        # switch options
+        transformation_filters = nf
+        switch_filters = nf
+        switch_reductions = 3
+        switch_processing_layers = 2
+        self.transformation_counts = 8
+        multiplx_fn = functools.partial(ConvBasisMultiplexer, transformation_filters, switch_filters, switch_reductions,
+                                        switch_processing_layers, self.transformation_counts)
+        pretransform_fn = functools.partial(ConvGnLelu, 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=3, depth=3,
+                                         weight_init_factor=.1)
+
+        # Feature branch
+        self.model_fea_conv = ConvGnLelu(in_nc, nf, kernel_size=3, norm=False, activation=False)
+        self.sw1 = ConfigurableSwitchComputer(transformation_filters, multiplx_fn,
+                                                   pre_transform_block=pretransform_fn, transform_block=transform_fn,
+                                                   attention_norm=True,
+                                                   transform_count=self.transformation_counts, init_temp=10,
+                                                   add_scalable_noise_to_transforms=True)
+        self.sw2 = ConfigurableSwitchComputer(transformation_filters, multiplx_fn,
+                                                   pre_transform_block=pretransform_fn, transform_block=transform_fn,
+                                                   attention_norm=True,
+                                                   transform_count=self.transformation_counts, init_temp=10,
+                                                   add_scalable_noise_to_transforms=True)
+        self.feature_lr_conv = ConvGnLelu(nf, nf, kernel_size=3, norm=False, activation=False)
+        self.feature_hr_conv1 = ConvGnLelu(nf, nf, kernel_size=3, norm=False, activation=True, bias=False)
+        self.feature_hr_conv2 = ConvGnLelu(nf, nf, kernel_size=3, norm=False, activation=False, bias=False)
+
+        # Grad branch
+        self.get_g_nopadding = ImageGradientNoPadding()
+        self.b_fea_conv = ConvGnLelu(in_nc, nf, kernel_size=3, norm=False, activation=False, bias=False)
+        self.sw_grad = ConfigurableSwitchComputer(transformation_filters, multiplx_fn,
+                                                   pre_transform_block=pretransform_fn, transform_block=transform_fn,
+                                                   attention_norm=True,
+                                                   transform_count=self.transformation_counts, init_temp=10,
+                                                   add_scalable_noise_to_transforms=True)
+        # Upsampling
+        self.grad_lr_conv = ConvGnLelu(nf, nf, kernel_size=3, norm=False, activation=True, bias=False)
+        grad_hr_conv1 = ConvGnLelu(nf, nf, kernel_size=3, norm=False, activation=True, bias=False)
+        grad_hr_conv2 = ConvGnLelu(nf, nf, kernel_size=3, norm=False, activation=False, bias=False)
+        self.branch_upsample = B.sequential(grad_hr_conv1, grad_hr_conv2)
+        # Conv used to output grad branch shortcut.
+        self.grad_branch_output_conv = ConvGnLelu(nf, out_nc, kernel_size=1, norm=False, activation=False, bias=False)
+
+        # Conjoin branch.
+        # Note: "_branch_pretrain" is a special tag used to denote parameters that get pretrained before the rest.
+        transform_fn_cat = functools.partial(MultiConvBlock, transformation_filters * 2, int(transformation_filters * 1.5),
+                                         transformation_filters, kernel_size=3, depth=4,
+                                         weight_init_factor=.1)
+        pretransform_fn_cat = functools.partial(ConvGnLelu, transformation_filters * 2, transformation_filters * 2, norm=False, bias=False, weight_init_factor=.1)
+        self._branch_pretrain_sw = ConfigurableSwitchComputer(transformation_filters, multiplx_fn,
+                                                   pre_transform_block=pretransform_fn_cat, transform_block=transform_fn_cat,
+                                                   attention_norm=True,
+                                                   transform_count=self.transformation_counts, init_temp=10,
+                                                   add_scalable_noise_to_transforms=True)
+        self.upsample = nn.Sequential(*[UpconvBlock(nf, nf, block=ConvGnLelu, norm=False, activation=False, bias=False) for _ in range(n_upscale)])
+        self.upsample_grad = nn.Sequential(*[UpconvBlock(nf, nf, block=ConvGnLelu, norm=False, activation=False, bias=False) for _ in range(n_upscale)])
+        self._branch_pretrain_lr_conv = ConvGnLelu(nf, nf, kernel_size=3, norm=False, activation=False)
+        self._branch_pretrain_HR_conv0 = ConvGnLelu(nf, nf, kernel_size=3, norm=False, activation=True, bias=False)
+        self._branch_pretrain_HR_conv1 = ConvGnLelu(nf, out_nc, kernel_size=3, norm=False, activation=False, bias=False)
+        self.switches = [self.sw1, self.sw2, self.sw_grad, self._branch_pretrain_sw]
+        self.attentions = None
+        self.init_temperature = 10
+        self.final_temperature_step = 10000
+
+    def forward(self, x):
+        x_grad = self.get_g_nopadding(x)
+        x = self.model_fea_conv(x)
+
+        x1, a1 = self.sw1(x, True)
+        x2, a2 = self.sw2(x1, True)
+        x_fea = self.feature_lr_conv(x2)
+        x_fea = self.feature_hr_conv1(x_fea)
+        x_fea = self.feature_hr_conv2(x_fea)
+
+        x_b_fea = self.b_fea_conv(x_grad)
+        x_grad, a3 = self.sw_grad(x_b_fea, att_in=x1, output_attention_weights=True)
+        x_grad = self.grad_lr_conv(x_grad)
+        x_grad = self.branch_upsample(x_grad)
+        x_out_branch = self.upsample_grad(x_grad)
+        x_out_branch = self.grad_branch_output_conv(x_out_branch)
+
+        x__branch_pretrain_cat = torch.cat([x_grad, x_fea], dim=1)
+        x__branch_pretrain_cat, a4 = self._branch_pretrain_sw(x__branch_pretrain_cat, att_in=x_fea, identity=x_fea, output_attention_weights=True)
+        x_out = self._branch_pretrain_lr_conv(x__branch_pretrain_cat)
+        x_out = self.upsample(x_out)
+        x_out = self._branch_pretrain_HR_conv0(x_out)
+        x_out = self._branch_pretrain_HR_conv1(x_out)
+
+        self.attentions = [a1, a2, a3, a4]
+
+        return x_out_branch, x_out, x_grad
+
+    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)
+            self.set_temperature(temp)
+            if step % 10 == 0:
+                output_path = os.path.join(experiments_path, "attention_maps", "a%i")
+                prefix = "attention_map_%i_%%i.png" % (step,)
+                [save_attention_to_image_rgb(output_path % (i,), self.attentions[i], self.transformation_counts, prefix, step) 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

codes/models/archs/SwitchedResidualGenerator_arch.py

@@ -134,11 +134,13 @@ class ConfigurableSwitchComputer(nn.Module):
         # depending on its needs.
         self.psc_scale = nn.Parameter(torch.full((1,), float(.1)))
 
-    def forward(self, x, output_attention_weights=False, att_in=None, fixed_scale=1):
+    def forward(self, x, output_attention_weights=False, identity=None, att_in=None, fixed_scale=1):
         if att_in is None:
             att_in = x
 
-        identity = x
+        if identity is None:
+            identity = x
+
         if self.add_noise:
             rand_feature = torch.randn_like(x) * self.noise_scale
             x = x + rand_feature

codes/models/networks.py

@@ -113,6 +113,8 @@ def define_G(opt, net_key='network_G'):
                             nb=opt_net['nb'], upscale=opt_net['scale'])
     elif which_model == "spsr_switched":
         netG = spsr.SwitchedSpsr(in_nc=3, out_nc=3, nf=opt_net['nf'], upscale=opt_net['scale'])
+    elif which_model == "spsr_switched_lr":
+        netG = spsr.SwitchedSpsrLr(in_nc=3, out_nc=3, nf=opt_net['nf'], upscale=opt_net['scale'])
 
     # image corruption
     elif which_model == 'HighToLowResNet':