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codes/models/archs/SPSR_arch.py

@@ -10,7 +10,7 @@ from utils.util import checkpoint
 
 from models.archs import SPSR_util as B
 from models.archs.SwitchedResidualGenerator_arch import ConfigurableSwitchComputer, ReferenceImageBranch, \
-    QueryKeyMultiplexer, QueryKeyPyramidMultiplexer
+    QueryKeyMultiplexer, QueryKeyPyramidMultiplexer, ConvBasisMultiplexer
 from models.archs.arch_util import ConvGnLelu, UpconvBlock, MultiConvBlock, ReferenceJoinBlock
 from switched_conv.switched_conv import compute_attention_specificity
 from switched_conv.switched_conv_util import save_attention_to_image_rgb
@@ -225,8 +225,6 @@ class Spsr5(nn.Module):
                                                    attention_norm=True,
                                                    transform_count=self.transformation_counts, init_temp=init_temperature,
                                                    add_scalable_noise_to_transforms=False, feed_transforms_into_multiplexer=True)
-        self.feature_lr_conv = ConvGnLelu(nf, nf, kernel_size=3, norm=True, activation=False)
-        self.feature_lr_conv2 = ConvGnLelu(nf, nf, kernel_size=3, norm=False, activation=False, bias=False)
 
         # Grad branch. Note - groupnorm on this branch is REALLY bad. Avoid it like the plague.
         self.get_g_nopadding = ImageGradientNoPadding()
@@ -726,3 +724,125 @@ class Spsr9(nn.Module):
             val["switch_%i_specificity" % (i,)] = means[i]
             val["switch_%i_histogram" % (i,)] = hists[i]
         return val
+
+
+class SwitchedSpsr(nn.Module):
+    def __init__(self, in_nc, nf, xforms=8, upscale=4, init_temperature=10):
+        super(SwitchedSpsr, 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 = xforms
+        multiplx_fn = functools.partial(ConvBasisMultiplexer, transformation_filters, switch_filters, switch_reductions,
+                                        switch_processing_layers, self.transformation_counts, use_exp2=True)
+        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=7, 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=init_temperature,
+                                                   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=init_temperature,
+                                                   add_scalable_noise_to_transforms=True)
+        self.feature_lr_conv = ConvGnLelu(nf, nf, kernel_size=3, norm=True, activation=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=7, norm=False, activation=False, bias=False)
+        mplex_grad = functools.partial(ConvBasisMultiplexer, nf * 2, nf * 2, switch_reductions,
+                                        switch_processing_layers, self.transformation_counts // 2, use_exp2=True)
+        self.sw_grad = ConfigurableSwitchComputer(transformation_filters, mplex_grad,
+                                                   pre_transform_block=pretransform_fn, transform_block=transform_fn,
+                                                   attention_norm=True,
+                                                   transform_count=self.transformation_counts // 2, init_temp=init_temperature,
+                                                   add_scalable_noise_to_transforms=True)
+        # Upsampling
+        self.grad_lr_conv = ConvGnLelu(nf, nf, kernel_size=3, norm=False, activation=True, bias=False)
+        self.grad_hr_conv = ConvGnLelu(nf, nf, kernel_size=3, norm=False, activation=False, bias=False)
+        # Conv used to output grad branch shortcut.
+        self.grad_branch_output_conv = ConvGnLelu(nf, 3, 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=init_temperature,
+                                                   add_scalable_noise_to_transforms=True)
+        self.upsample = nn.Sequential(*[UpconvBlock(nf, nf, block=ConvGnLelu, norm=False, activation=False, bias=True) for _ in range(n_upscale)])
+        self.upsample_grad = nn.Sequential(*[UpconvBlock(nf, nf, block=ConvGnLelu, norm=False, activation=False, bias=True) for _ in range(n_upscale)])
+        self.final_lr_conv = ConvGnLelu(nf, nf, kernel_size=3, norm=True, activation=False)
+        self.final_hr_conv1 = ConvGnLelu(nf, nf, kernel_size=3, norm=True, activation=True, bias=True)
+        self.final_hr_conv2 = ConvGnLelu(nf, 3, 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 = init_temperature
+        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, do_checkpointing=True)
+        x2, a2 = self.sw2(x1, do_checkpointing=True)
+        x_fea = self.feature_lr_conv(x2)
+        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=torch.cat([x1, x_b_fea], dim=1), output_attention_weights=True, do_checkpointing=True)
+        x_grad = checkpoint(self.grad_lr_conv, x_grad)
+        x_grad = checkpoint(self.grad_hr_conv, x_grad)
+        x_out_branch = checkpoint(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 = checkpoint(self.final_lr_conv, x__branch_pretrain_cat)
+        x_out = checkpoint(self.upsample, x_out)
+        x_out = checkpoint(self.final_hr_conv1, x_out)
+        x_out = self.final_hr_conv2(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 % 200 == 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, net):
+        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

@@ -79,7 +79,8 @@ def gather_2d(input, index):
 
 class ConfigurableSwitchComputer(nn.Module):
     def __init__(self, base_filters, multiplexer_net, pre_transform_block, transform_block, transform_count, attention_norm,
-                 init_temp=20, add_scalable_noise_to_transforms=False, feed_transforms_into_multiplexer=False):
+                 init_temp=20, add_scalable_noise_to_transforms=False, feed_transforms_into_multiplexer=False, post_switch_conv=True,
+                 anorm_multiplier=16):
         super(ConfigurableSwitchComputer, self).__init__()
 
         tc = transform_count
@@ -95,12 +96,15 @@ class ConfigurableSwitchComputer(nn.Module):
         self.noise_scale = nn.Parameter(torch.full((1,), float(1e-3)))
 
         # And the switch itself, including learned scalars
-        self.switch = BareConvSwitch(initial_temperature=init_temp, attention_norm=AttentionNorm(transform_count, accumulator_size=16 * transform_count) if attention_norm else None)
+        self.switch = BareConvSwitch(initial_temperature=init_temp, attention_norm=AttentionNorm(transform_count, accumulator_size=anorm_multiplier * transform_count) if attention_norm else None)
         self.switch_scale = nn.Parameter(torch.full((1,), float(1)))
-        self.post_switch_conv = ConvBnLelu(base_filters, base_filters, norm=False, bias=True)
+        if post_switch_conv:
-        # The post_switch_conv gets a low scale initially. The network can decide to magnify it (or not)
+            self.post_switch_conv = ConvBnLelu(base_filters, base_filters, norm=False, bias=True)
-        # depending on its needs.
+            # The post_switch_conv gets a low scale initially. The network can decide to magnify it (or not)
-        self.psc_scale = nn.Parameter(torch.full((1,), float(.1)))
+            # depending on its needs.
+            self.psc_scale = nn.Parameter(torch.full((1,), float(.1)))
+        else:
+            self.post_switch_conv = None
         self.update_norm = True
 
     def set_update_attention_norm(self, set_val):
@@ -151,7 +155,8 @@ class ConfigurableSwitchComputer(nn.Module):
         # It is assumed that [xformed] and [m] are collapsed into tensors at this point.
         outputs, attention, att_logits = self.switch(xformed, m, True, self.update_norm, output_attention_logits=True)
         outputs = identity + outputs * self.switch_scale * fixed_scale
-        outputs = outputs + self.post_switch_conv(outputs) * self.psc_scale * fixed_scale
+        if self.post_switch_conv is not None:
+            outputs = outputs + self.post_switch_conv(outputs) * self.psc_scale * fixed_scale
         if output_attention_weights:
             if output_att_logits:
                 return outputs, attention, att_logits
@@ -642,7 +647,8 @@ class TheBigSwitch(SwitchModelBase):
                                                    pre_transform_block=None, transform_block=transform_fn,
                                                    attention_norm=True,
                                                    transform_count=self.transformation_counts, init_temp=init_temperature,
-                                                   add_scalable_noise_to_transforms=False, feed_transforms_into_multiplexer=True)
+                                                   add_scalable_noise_to_transforms=False, feed_transforms_into_multiplexer=True,
+                                                   anorm_multiplier=128)
         self.switches = [self.switch]
 
         self.final_lr_conv = ConvGnLelu(nf, nf, kernel_size=3, norm=False, activation=True, bias=True)
@@ -670,6 +676,64 @@ class TheBigSwitch(SwitchModelBase):
         return x_out,
 
 
+class ArtistMultiplexer(nn.Module):
+    def __init__(self, in_nc, nf, multiplexer_channels):
+        super(ArtistMultiplexer, self).__init__()
+
+        self.spine = SpineNet(arch='96', output_level=[3], double_reduce_early=False)
+        self.spine_red_proc = ConvGnSilu(256, nf, kernel_size=1, activation=False, norm=False, bias=False)
+        self.fea_tail = ConvGnSilu(in_nc, nf, kernel_size=7, bias=True, norm=False, activation=False)
+        self.tail_proc = make_res_layer(BasicBlock, nf, nf, 2)
+        self.tail_join = ReferenceJoinBlock(nf)
+
+        self.reduce = ConvGnSilu(nf, nf // 2, kernel_size=1, activation=True, norm=True, bias=False)
+        self.last_process = ConvGnSilu(nf // 2, nf // 2, kernel_size=1, activation=True, norm=False, bias=False)
+        self.to_attention = ConvGnSilu(nf // 2, multiplexer_channels, kernel_size=1, activation=False, norm=False, bias=False)
+
+    def forward(self, x, transformations):
+        s = self.spine(x)[0]
+        tail = self.fea_tail(x)
+        tail = self.tail_proc(tail)
+        q = F.interpolate(s, scale_factor=2, mode='nearest')
+        q = self.spine_red_proc(q)
+        q, _ = self.tail_join(q, tail)
+        q = self.reduce(q)
+        q = F.interpolate(q, scale_factor=2, mode='nearest')
+        return self.to_attention(self.last_process(q))
+
+
+class ArtistGen(SwitchModelBase):
+    def __init__(self, in_nc, nf, xforms=16, upscale=2, init_temperature=10):
+        super(ArtistGen, self).__init__(init_temperature, 10000)
+        self.nf = nf
+        self.transformation_counts = xforms
+
+        multiplx_fn = functools.partial(ArtistMultiplexer, in_nc, nf)
+        transform_fn = functools.partial(MultiConvBlock, in_nc, int(in_nc * 2), in_nc, kernel_size=3, depth=4, weight_init_factor=.1)
+        self.switch = ConfigurableSwitchComputer(nf, multiplx_fn,
+                                                   pre_transform_block=None, transform_block=transform_fn,
+                                                   attention_norm=True,
+                                                   transform_count=self.transformation_counts, init_temp=init_temperature,
+                                                   add_scalable_noise_to_transforms=False, feed_transforms_into_multiplexer=True,
+                                                   anorm_multiplier=128, post_switch_conv=False)
+        self.switches = [self.switch]
+
+    def forward(self, x, 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)
+
+        up = F.interpolate(x, scale_factor=2, mode="bicubic")
+        out, a1, att_logits = self.switch(up, att_in=x, do_checkpointing=True, output_att_logits=True)
+
+        if save_attentions:
+            self.attentions = [a1]
+        return out, att_logits.permute(0,3,1,2)
+
 if __name__ == '__main__':
     tbs = TheBigSwitch(3, 64)
     x = torch.randn(4,3,64,64)

codes/models/networks.py

@@ -67,6 +67,8 @@ def define_G(opt, net_key='network_G', scale=None):
     elif which_model == 'spsr_net_improved':
         netG = spsr.SPSRNetSimplified(in_nc=opt_net['in_nc'], out_nc=opt_net['out_nc'], nf=opt_net['nf'],
                             nb=opt_net['nb'], upscale=opt_net['scale'])
+    elif which_model == "spsr_switched":
+        netG = spsr.SwitchedSpsr(in_nc=3, nf=opt_net['nf'], upscale=opt_net['scale'], init_temperature=opt_net['temperature'])
     elif which_model == "spsr5":
         xforms = opt_net['num_transforms'] if 'num_transforms' in opt_net.keys() else 8
         netG = spsr.Spsr5(in_nc=3, out_nc=3, nf=opt_net['nf'], xforms=xforms, upscale=opt_net['scale'],
@@ -112,6 +114,9 @@ def define_G(opt, net_key='network_G', scale=None):
     elif which_model == 'big_switch':
         netG = SwitchedGen_arch.TheBigSwitch(opt_net['in_nc'], nf=opt_net['nf'], xforms=opt_net['num_transforms'], upscale=opt_net['scale'],
                                              init_temperature=opt_net['temperature'])
+    elif which_model == 'artist':
+        netG = SwitchedGen_arch.ArtistGen(opt_net['in_nc'], nf=opt_net['nf'], xforms=opt_net['num_transforms'], upscale=opt_net['scale'],
+                                             init_temperature=opt_net['temperature'])
     elif which_model == "flownet2":
         from models.flownet2.models import FlowNet2
         ld = torch.load(opt_net['load_path'])