Add growth channel to switch_growths for flat networks

codes/models/archs/SwitchedResidualGenerator_arch.py

@@ -3,6 +3,7 @@ from torch import nn
 from switched_conv import BareConvSwitch, compute_attention_specificity
 import torch.nn.functional as F
 import functools
+from collections import OrderedDict
 from models.archs.arch_util import initialize_weights
 from switched_conv_util import save_attention_to_image
 
@@ -61,13 +62,24 @@ class HalvingProcessingBlock(nn.Module):
         return self.bnconv2(x)
 
 
+# Creates a nested series of convolutional blocks. Each block processes the input data in-place and adds
+# filter_growth filters. Return is (nn.Sequential, ending_filters)
+def create_sequential_growing_processing_block(filters_init, filter_growth, num_convs):
+    convs = []
+    current_filters = filters_init
+    for i in range(num_convs):
+        convs.append(ConvBnLelu(current_filters, current_filters + filter_growth))
+        current_filters += filter_growth
+    return nn.Sequential(*convs), current_filters
+
+
 class SwitchComputer(nn.Module):
-    def __init__(self, channels_in, filters, transform_block, transform_count, reduction_blocks, processing_blocks=0, init_temp=20):
+    def __init__(self, channels_in, filters, growth, transform_block, transform_count, reduction_blocks, processing_blocks=0, init_temp=20):
         super(SwitchComputer, self).__init__()
         self.filter_conv = ConvBnLelu(channels_in, filters)
         self.reduction_blocks = nn.ModuleList([HalvingProcessingBlock(filters * 2 ** i) for i in range(reduction_blocks)])
         final_filters = filters * 2 ** reduction_blocks
-        self.processing_blocks = nn.ModuleList([ConvBnLelu(final_filters, final_filters) for i in range(processing_blocks)])
+        self.processing_blocks, final_filters = create_sequential_growing_processing_block(final_filters, growth, processing_blocks)
         proc_block_filters = max(final_filters // 2, transform_count)
         self.proc_switch_conv = ConvBnLelu(final_filters, proc_block_filters)
         self.final_switch_conv = nn.Conv2d(proc_block_filters, transform_count, 1, 1, 0)
@@ -104,13 +116,13 @@ class SwitchComputer(nn.Module):
 
 
 class ConfigurableSwitchedResidualGenerator(nn.Module):
-    def __init__(self, switch_filters, switch_reductions, switch_processing_layers, trans_counts, trans_kernel_sizes,
+    def __init__(self, switch_filters, switch_growths, switch_reductions, switch_processing_layers, trans_counts, trans_kernel_sizes,
                  trans_layers, trans_filters_mid, initial_temp=20, final_temperature_step=50000, heightened_temp_min=1,
                  heightened_final_step=50000, upsample_factor=1):
         super(ConfigurableSwitchedResidualGenerator, self).__init__()
         switches = []
-        for filters, sw_reduce, sw_proc, trans_count, kernel, layers, mid_filters in zip(switch_filters, switch_reductions, switch_processing_layers, trans_counts, trans_kernel_sizes, trans_layers, trans_filters_mid):
+        for filters, growth, sw_reduce, sw_proc, trans_count, kernel, layers, mid_filters in zip(switch_filters, switch_growths, switch_reductions, switch_processing_layers, trans_counts, trans_kernel_sizes, trans_layers, trans_filters_mid):
-            switches.append(SwitchComputer(3, filters, functools.partial(ResidualBranch, 3, mid_filters, 3, kernel_size=kernel, depth=layers), trans_count, sw_reduce, sw_proc, initial_temp))
+            switches.append(SwitchComputer(3, filters, growth, functools.partial(ResidualBranch, 3, mid_filters, 3, kernel_size=kernel, depth=layers), trans_count, sw_reduce, sw_proc, initial_temp))
         initialize_weights(switches, 1)
         # Initialize the transforms with a lesser weight, since they are repeatedly added on to the resultant image.
         initialize_weights([s.transforms for s in switches], .2 / len(switches))

codes/models/networks.py

@@ -63,7 +63,8 @@ def define_G(opt, net_key='network_G'):
                                         final_temperature_step=opt_net['temperature_final_step'])
         netG = RRDBNet_arch.PixShuffleRRDB(nf=opt_net['nf'], nb=opt_net['nb'], gc=opt_net['gc'], scale=scale, rrdb_block_f=block_f)
     elif which_model == "ConfigurableSwitchedResidualGenerator":
-        netG = SwitchedGen_arch.ConfigurableSwitchedResidualGenerator(switch_filters=opt_net['switch_filters'], switch_reductions=opt_net['switch_reductions'],
+        netG = SwitchedGen_arch.ConfigurableSwitchedResidualGenerator(switch_filters=opt_net['switch_filters'], switch_growths=opt_net['switch_growths'],
+                                                                      switch_reductions=opt_net['switch_reductions'],
                                                                       switch_processing_layers=opt_net['switch_processing_layers'], trans_counts=opt_net['trans_counts'],
                                                                       trans_kernel_sizes=opt_net['trans_kernel_sizes'], trans_layers=opt_net['trans_layers'],
                                                                       trans_filters_mid=opt_net['trans_filters_mid'],