SSGSimpler network

codes/models/archs/StructuredSwitchedGenerator.py

@@ -1,17 +1,16 @@
-import math
 import functools
-from models.archs.arch_util import MultiConvBlock, ConvGnLelu, ConvGnSilu, ReferenceJoinBlock
+import math
-from models.archs.SwitchedResidualGenerator_arch import ConfigurableSwitchComputer, gather_2d, SwitchModelBase
+
-from models.archs.SPSR_arch import ImageGradientNoPadding
-from torch import nn
 import torch
 import torch.nn.functional as F
-from switched_conv.switched_conv_util import save_attention_to_image_rgb
+from torch import nn
-from switched_conv.switched_conv import compute_attention_specificity
+
-import os
+from models.archs.SPSR_arch import ImageGradientNoPadding
-import torchvision
+from models.archs.SwitchedResidualGenerator_arch import ConfigurableSwitchComputer, gather_2d, SwitchModelBase
+from models.archs.arch_util import MultiConvBlock, ConvGnLelu, ConvGnSilu, ReferenceJoinBlock
 from utils.util import checkpoint
 
+
 # VGG-style layer with Conv(stride2)->BN->Activation->Conv->BN->Activation
 # Doubles the input filter count.
 class HalvingProcessingBlock(nn.Module):
@@ -446,3 +445,132 @@ class StackedSwitchGenerator2xTeco(SwitchModelBase):
             self.attentions = [a1, a3, a3, a4, a5]
         return x_out,
 
+
+class SimplePyramidMultiplexer(nn.Module):
+    def __init__(self, nf, transforms):
+        super(SimplePyramidMultiplexer, self).__init__()
+
+        # Blocks used to create the query
+        reductions = 3
+        self.input_process = ConvGnSilu(nf, nf, activation=True, norm=False, bias=True)
+        self.reduction_blocks = nn.ModuleList([HalvingProcessingBlock(int(nf * 1.5 ** i), factor=1.5)
+                                               for i in range(reductions)])
+        reduction_filters = int(nf * 1.5 ** reductions)
+        self.processing_blocks = nn.Sequential(
+            ConvGnSilu(reduction_filters, reduction_filters, kernel_size=3, activation=True, norm=True, bias=False),
+            ConvGnSilu(reduction_filters, reduction_filters, kernel_size=3, activation=True, norm=True, bias=False))
+        self.expansion_blocks = nn.ModuleList([ExpansionBlock2(int(reduction_filters // (1.5 ** i)), factor=1.5)
+                                               for i in range(reductions)])
+
+        self.cbl1 = ConvGnSilu(nf, nf // 2, kernel_size=1, norm=False, bias=False)
+        self.cbl2 = ConvGnSilu(nf // 2, transforms, kernel_size=1, norm=False, bias=False)
+
+    def forward(self, x):
+        q = self.input_process(x)
+        reduction_identities = []
+        for b in self.reduction_blocks:
+            reduction_identities.append(q)
+            q = b(q)
+        q = self.processing_blocks(q)
+        for i, b in enumerate(self.expansion_blocks):
+            q = b(q, reduction_identities[-i - 1])
+        q = self.cbl1(q)
+        q = self.cbl2(q)
+        return q
+
+
+class SimplerSwitchWithReference(nn.Module):
+    def __init__(self, nf, num_transforms, init_temperature=10, has_ref=True):
+        super(SimplerSwitchWithReference, self).__init__()
+        self.nf = nf
+        self.transformation_counts = num_transforms
+        multiplx_fn = functools.partial(SimplePyramidMultiplexer, nf)
+        pretransform = functools.partial(ConvGnLelu, nf, int(nf*1.5), kernel_size=3, bias=False, norm=False, activation=True, weight_init_factor=.1)
+        transform_fn = functools.partial(ConvGnLelu, int(nf * 1.5), int(nf * 1.5), kernel_size=3, bias=False, norm=False, activation=True, weight_init_factor=.1)
+        posttransform = ConvGnLelu(int(nf*1.5), nf, kernel_size=3, bias=False, norm=False, activation=True, weight_init_factor=.1)
+        if has_ref:
+            self.ref_join = ReferenceJoinBlock(nf, residual_weight_init_factor=.3, final_norm=False, kernel_size=1, depth=2)
+        else:
+            self.ref_join = None
+        self.switch = ConfigurableSwitchComputer(nf, multiplx_fn,
+                                                   pre_transform_block=pretransform, transform_block=transform_fn,
+                                                   post_transform_block=posttransform,
+                                                   attention_norm=True,
+                                                   transform_count=self.transformation_counts, init_temp=init_temperature,
+                                                   add_scalable_noise_to_transforms=False, feed_transforms_into_multiplexer=False)
+
+    def forward(self, x, ref=None):
+        if self.ref_join is not None:
+            branch, ref_std = self.ref_join(x, ref)
+            return self.switch(branch, identity=x) + (ref_std,)
+        else:
+            return self.switch(x, identity=x)
+
+
+class SsgSimpler(SwitchModelBase):
+    def __init__(self, in_nc, out_nc, nf, xforms=8, init_temperature=10, recurrent=False):
+        super(SsgSimpler, self).__init__(init_temperature, 10000)
+        self.nf = nf
+
+        # processing the input embedding
+        if recurrent:
+            self.recurrent = True
+            self.recurrent_process = ConvGnLelu(in_nc, nf, kernel_size=3, stride=2, norm=False, bias=True, activation=False)
+            self.recurrent_join = ReferenceJoinBlock(nf, residual_weight_init_factor=.01, final_norm=False, kernel_size=1, depth=3, join=False)
+        else:
+            self.recurrent = False
+
+        # Feature branch
+        self.model_fea_conv = ConvGnLelu(in_nc, nf, kernel_size=7, norm=False, activation=False)
+        self.sw1 = SimplerSwitchWithReference(nf, xforms, init_temperature, has_ref=False)
+        self.sw2 = SimplerSwitchWithReference(nf, xforms, init_temperature, has_ref=False)
+
+        # Grad branch. Note - groupnorm on this branch is REALLY bad. Avoid it like the plague.
+        self.get_g_nopadding = ImageGradientNoPadding()
+        self.grad_conv = ConvGnLelu(in_nc, nf, kernel_size=7, norm=False, activation=False, bias=False)
+        self.sw_grad = SimplerSwitchWithReference(nf, xforms // 2, init_temperature, has_ref=True)
+        self.grad_lr_conv = ConvGnLelu(nf, nf, kernel_size=3, norm=False, activation=True, bias=True)
+        self.upsample_grad = UpconvBlock(nf, nf // 2, block=ConvGnLelu, norm=False, activation=True, bias=False)
+        self.grad_branch_output_conv = ConvGnLelu(nf // 2, out_nc, kernel_size=1, norm=False, activation=False, bias=True)
+
+        # Join branch (grad+fea)
+        self.conjoin_sw = SimplerSwitchWithReference(nf, xforms, init_temperature, has_ref=True)
+        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, out_nc, kernel_size=3, norm=False, activation=False, bias=False)
+        self.switches = [self.sw1.switch, self.sw2.switch, self.sw_grad.switch, self.conjoin_sw.switch]
+
+    def forward(self, x, save_attentions=True, recurrent=None):
+        # 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)
+
+        x1 = self.model_fea_conv(x)
+        if self.recurrent:
+            rec = self.recurrent_process(recurrent)
+            x1, recurrent_std = self.recurrent_join(x1, rec)
+        x1, a1 = checkpoint(self.sw1, x1)
+        x2, a2 = checkpoint(self.sw2, x1)
+
+        x_grad = self.get_g_nopadding(x)
+        x_grad = self.grad_conv(x_grad)
+        x_grad, a3, grad_fea_std = checkpoint(self.sw_grad, x_grad, x1)
+        x_grad = checkpoint(self.grad_lr_conv, x_grad)
+        x_grad_out = checkpoint(self.upsample_grad, x_grad)
+        x_grad_out = checkpoint(self.grad_branch_output_conv, x_grad_out)
+
+        x3, a4, fea_grad_std = checkpoint(self.conjoin_sw, x2, x_grad)
+        x_out = checkpoint(self.final_lr_conv, x3)
+        x_out = checkpoint(self.upsample, x_out)
+        x_out = checkpoint(self.final_hr_conv2, x_out)
+
+        if save_attentions:
+            self.attentions = [a1, a2, a3, a4]
+        self.grad_fea_std = grad_fea_std.detach().cpu()
+        self.fea_grad_std = fea_grad_std.detach().cpu()
+        return x_grad_out, x_out

codes/models/archs/SwitchedResidualGenerator_arch.py

@@ -79,6 +79,7 @@ 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,
+                 post_transform_block=None,
                  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__()
@@ -98,6 +99,8 @@ class ConfigurableSwitchComputer(nn.Module):
         # And the switch itself, including learned scalars
         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)))
+        if post_transform_block is not None:
+            self.post_transform_block = post_transform_block
         if post_switch_conv:
             self.post_switch_conv = ConvBnLelu(base_filters, base_filters, norm=False, bias=True)
             # The post_switch_conv gets a low scale initially. The network can decide to magnify it (or not)
@@ -154,6 +157,9 @@ 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)
+        if self.post_transform_block is not None:
+            outputs = self.post_transform_block(outputs)
+
         outputs = identity + outputs * self.switch_scale * fixed_scale
         if self.post_switch_conv is not None:
             outputs = outputs + self.post_switch_conv(outputs) * self.psc_scale * fixed_scale

codes/models/networks.py

@@ -109,6 +109,10 @@ 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_simpler':
+        xforms = opt_net['num_transforms'] if 'num_transforms' in opt_net.keys() else 8
+        netG = ssg.SsgSimpler(in_nc=3, out_nc=3, nf=opt_net['nf'], xforms=xforms,
+                                 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 == 'big_switch':

codes/train.py

@@ -30,7 +30,7 @@ def init_dist(backend='nccl', **kwargs):
 def main():
     #### options
     parser = argparse.ArgumentParser()
-    parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/train_exd_imgset_bigswitch.yml')
+    parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/train_exd_imgset_ssgsimpler.yml')
     parser.add_argument('--launcher', choices=['none', 'pytorch'], default='none', help='job launcher')
     parser.add_argument('--local_rank', type=int, default=0)
     args = parser.parse_args()