SPSR LR2

codes/models/SRGAN_model.py

@@ -494,10 +494,11 @@ class SRGANModel(BaseModel):
 
                 if self.spsr_enabled and self.cri_grad_gan:
                     if self.opt['train']['gan_type'] == 'crossgan':
-                        pred_g_fake_grad = self.netD(fake_H_grad, var_L)
+                        pred_g_fake_grad = self.netD_grad(fake_H_grad, var_L)
+                        pred_g_fake_grad_branch = self.netD_grad(fake_H_branch, var_L)
                     else:
-                        pred_g_fake_grad = self.netD(fake_H_grad)
+                        pred_g_fake_grad = self.netD_grad(fake_H_grad)
-                    pred_g_fake_grad_branch = self.netD_grad(fake_H_branch)
+                        pred_g_fake_grad_branch = self.netD_grad(fake_H_branch)
                     if self.opt['train']['gan_type'] in ['gan', 'pixgan', 'pixgan_fea', 'crossgan']:
                         l_g_gan_grad = self.l_gan_grad_w * self.cri_grad_gan(pred_g_fake_grad, True)
                         l_g_gan_grad_branch = self.l_gan_grad_w * self.cri_grad_gan(pred_g_fake_grad_branch, True)
@@ -685,19 +686,23 @@ class SRGANModel(BaseModel):
                 for p in self.netD_grad.parameters():
                     p.requires_grad = True
                 self.optimizer_D_grad.zero_grad()
-                for var_ref, fake_H, fake_H_grad_branch in zip(var_ref_skips, self.fake_H, self.spsr_grad_GenOut):
+                for var_L, var_ref, fake_H, fake_H_grad_branch in zip(self.var_L, var_ref_skips, self.fake_H, self.spsr_grad_GenOut):
                     fake_H_grad = self.get_grad_nopadding(fake_H).detach()
                     var_ref_grad = self.get_grad_nopadding(var_ref)
-                    pred_d_real_grad = self.netD_grad(var_ref_grad)
-                    pred_d_fake_grad = self.netD_grad(fake_H_grad)   # Tensor already detached above.
-                    # var_ref and fake_H already has noise added to it. We **must** add noise to fake_H_grad_branch too.
                     fake_H_grad_branch = fake_H_grad_branch.detach() + noise
-                    pred_d_fake_grad_branch = self.netD_grad(fake_H_grad_branch)
+                    if self.opt['train']['gan_type'] == 'crossgan':
-                    if self.opt['train']['gan_type'] == 'gan':
+                        pred_d_real_grad = self.netD_grad(var_ref_grad, var_L)
+                        pred_d_fake_grad = self.netD_grad(fake_H_grad, var_L)   # Tensor already detached above.
+                        # var_ref and fake_H already has noise added to it. We **must** add noise to fake_H_grad_branch too.
+                        pred_d_fake_grad_branch = self.netD_grad(fake_H_grad_branch, var_L)
+                    else:
+                        pred_d_real_grad = self.netD_grad(var_ref_grad)
+                        pred_d_fake_grad = self.netD_grad(fake_H_grad)   # Tensor already detached above.
+                        # var_ref and fake_H already has noise added to it. We **must** add noise to fake_H_grad_branch too.
+                        pred_d_fake_grad_branch = self.netD_grad(fake_H_grad_branch)
+                    if self.opt['train']['gan_type'] == 'gan' or self.opt['train']['gan_type'] == 'crossgan':
                         l_d_real_grad = self.cri_gan(pred_d_real_grad, True)
                         l_d_fake_grad = (self.cri_gan(pred_d_fake_grad, False) + self.cri_gan(pred_d_fake_grad_branch, False)) / 2
-                    elif self.opt['train']['gan_type'] == 'crossgan':
-                        assert False
                     elif self.opt['train']['gan_type'] == 'pixgan':
                         real = torch.ones_like(pred_d_real_grad)
                         fake = torch.zeros_like(pred_d_fake_grad)

codes/models/archs/SPSR_arch.py

@@ -670,3 +670,125 @@ class SwitchedSpsrLr(nn.Module):
             val["switch_%i_specificity" % (i,)] = means[i]
             val["switch_%i_histogram" % (i,)] = hists[i]
         return val
+
+
+class SwitchedSpsrLr2(nn.Module):
+    def __init__(self, in_nc, out_nc, nf, upscale=4):
+        super(SwitchedSpsrLr2, 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, 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=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=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=3, 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=10,
+                                                   add_scalable_noise_to_transforms=True)
+        # Upsampling
+        self.grad_lr_conv = ConvGnLelu(nf, nf, kernel_size=3, norm=True, 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, 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.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=False)
+        self.final_hr_conv2 = 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_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)
+        x_grad = self.grad_lr_conv(x_grad)
+        x_grad = self.grad_hr_conv(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.final_lr_conv(x__branch_pretrain_cat)
+        x_out = self.upsample(x_out)
+        x_out = 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):
+        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

@@ -4,7 +4,7 @@ from switched_conv import BareConvSwitch, compute_attention_specificity, Attenti
 import torch.nn.functional as F
 import functools
 from collections import OrderedDict
-from models.archs.arch_util import ConvBnLelu, ConvGnSilu, ExpansionBlock
+from models.archs.arch_util import ConvBnLelu, ConvGnSilu, ExpansionBlock, ExpansionBlock2
 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_rgb
@@ -47,13 +47,16 @@ class HalvingProcessingBlock(nn.Module):
 # This is a classic u-net architecture with the goal of assigning each individual pixel an individual transform
 # switching set.
 class ConvBasisMultiplexer(nn.Module):
-    def __init__(self, input_channels, base_filters, reductions, processing_depth, multiplexer_channels, use_gn=True):
+    def __init__(self, input_channels, base_filters, reductions, processing_depth, multiplexer_channels, use_gn=True, use_exp2=False):
         super(ConvBasisMultiplexer, self).__init__()
         self.filter_conv = ConvGnSilu(input_channels, base_filters, bias=True)
         self.reduction_blocks = nn.ModuleList([HalvingProcessingBlock(base_filters * 2 ** i) for i in range(reductions)])
         reduction_filters = base_filters * 2 ** reductions
         self.processing_blocks = nn.Sequential(OrderedDict([('block%i' % (i,), ConvGnSilu(reduction_filters, reduction_filters, bias=False)) for i in range(processing_depth)]))
-        self.expansion_blocks = nn.ModuleList([ExpansionBlock(reduction_filters // (2 ** i)) for i in range(reductions)])
+        if use_exp2:
+            self.expansion_blocks = nn.ModuleList([ExpansionBlock2(reduction_filters // (2 ** i)) for i in range(reductions)])
+        else:
+            self.expansion_blocks = nn.ModuleList([ExpansionBlock(reduction_filters // (2 ** i)) for i in range(reductions)])
 
         gap = base_filters - multiplexer_channels
         cbl1_out = ((base_filters - (gap // 2)) // 4) * 4   # Must be multiples of 4 to use with group norm.

codes/models/archs/arch_util.py

@@ -391,6 +391,30 @@ class ExpansionBlock(nn.Module):
         return self.process(x)
 
 
+# Block that upsamples 2x and reduces incoming filters by 2x. It preserves structure by taking a passthrough feed
+# along with the feature representation.
+# Differs from ExpansionBlock because it performs all processing in 2xfilter space and decimates at the last step.
+class ExpansionBlock2(nn.Module):
+    def __init__(self, filters_in, filters_out=None, block=ConvGnSilu):
+        super(ExpansionBlock2, self).__init__()
+        if filters_out is None:
+            filters_out = filters_in // 2
+        self.decimate = block(filters_in, filters_out, kernel_size=1, bias=False, activation=False, norm=True)
+        self.process_passthrough = block(filters_out, filters_out, kernel_size=3, bias=True, activation=False, norm=True)
+        self.conjoin = block(filters_out*2, filters_out*2, kernel_size=3, bias=False, activation=True, norm=False)
+        self.reduce = block(filters_out*2, filters_out, kernel_size=3, bias=False, activation=True, norm=True)
+
+    # input is the feature signal with shape  (b, f, w, h)
+    # passthrough is the structure signal with shape (b, f/2, w*2, h*2)
+    # output is conjoined upsample with shape (b, f/2, w*2, h*2)
+    def forward(self, input, passthrough):
+        x = F.interpolate(input, scale_factor=2, mode="nearest")
+        x = self.decimate(x)
+        p = self.process_passthrough(passthrough)
+        x = self.conjoin(torch.cat([x, p], dim=1))
+        return self.reduce(x)
+
+
 # Similar to ExpansionBlock but does not upsample.
 class ConjoinBlock(nn.Module):
     def __init__(self, filters_in, filters_out=None, block=ConvGnSilu, norm=True):

codes/models/networks.py

@@ -115,6 +115,8 @@ def define_G(opt, net_key='network_G'):
         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'])
+    elif which_model == "spsr_switched_lr2":
+        netG = spsr.SwitchedSpsrLr2(in_nc=3, out_nc=3, nf=opt_net['nf'], upscale=opt_net['scale'])
 
     # image corruption
     elif which_model == 'HighToLowResNet':