diff --git a/codes/models/archs/NestedSwitchGenerator.py b/codes/models/archs/NestedSwitchGenerator.py index ec2d4dc8..7510c7e2 100644 --- a/codes/models/archs/NestedSwitchGenerator.py +++ b/codes/models/archs/NestedSwitchGenerator.py @@ -83,9 +83,9 @@ class Constrictor(nn.Module): assert(filters > output_filters) gap = filters - output_filters gap_div_4 = int(gap / 4) - self.cbl1 = ConvBnRelu(filters, filters - (gap_div_4 * 2), kernel_size=1, bn=True, bias=True) - self.cbl2 = ConvBnRelu(filters - (gap_div_4 * 2), filters - (gap_div_4 * 3), kernel_size=1, bn=True, bias=False) - self.cbl3 = ConvBnRelu(filters - (gap_div_4 * 3), output_filters, kernel_size=1, relu=False, bn=False, bias=False) + self.cbl1 = ConvBnRelu(filters, filters - (gap_div_4 * 2), kernel_size=1, norm=True, bias=True) + self.cbl2 = ConvBnRelu(filters - (gap_div_4 * 2), filters - (gap_div_4 * 3), kernel_size=1, norm=True, bias=False) + self.cbl3 = ConvBnRelu(filters - (gap_div_4 * 3), output_filters, kernel_size=1, activation=False, norm=False, bias=False) def forward(self, x): x = self.cbl1(x) @@ -134,10 +134,10 @@ class NestedSwitchComputer(nn.Module): filters.append(current_filters) reduce = True - self.multiplexer_init_conv = ConvBnLelu(transform_filters, switch_base_filters, kernel_size=7, lelu=False, bn=False) + self.multiplexer_init_conv = ConvBnLelu(transform_filters, switch_base_filters, kernel_size=7, activation=False, norm=False) self.processing_trunk = nn.ModuleList(processing_trunk) self.switch = RecursiveSwitchedTransform(transform_filters, filters, nesting_depth-1, transforms_at_leaf, trans_kernel_size, trans_num_layers-1, trans_scale_init, initial_temp=initial_temp, add_scalable_noise_to_transforms=add_scalable_noise_to_transforms) - self.anneal = ConvBnLelu(transform_filters, transform_filters, kernel_size=1, bn=False) + self.anneal = ConvBnLelu(transform_filters, transform_filters, kernel_size=1, norm=False) def forward(self, x): feed_forward = x @@ -161,9 +161,9 @@ class NestedSwitchedGenerator(nn.Module): trans_layers, transformation_filters, initial_temp=20, final_temperature_step=50000, heightened_temp_min=1, heightened_final_step=50000, upsample_factor=1, add_scalable_noise_to_transforms=False): super(NestedSwitchedGenerator, self).__init__() - self.initial_conv = ConvBnLelu(3, transformation_filters, kernel_size=7, lelu=False, bn=False) - self.proc_conv = ConvBnLelu(transformation_filters, transformation_filters, bn=False) - self.final_conv = ConvBnLelu(transformation_filters, 3, kernel_size=1, lelu=False, bn=False) + self.initial_conv = ConvBnLelu(3, transformation_filters, kernel_size=7, activation=False, norm=False) + self.proc_conv = ConvBnLelu(transformation_filters, transformation_filters, norm=False) + self.final_conv = ConvBnLelu(transformation_filters, 3, kernel_size=1, activation=False, norm=False) switches = [] for sw_reduce, sw_proc, trans_count, kernel, layers in zip(switch_reductions, switch_processing_layers, trans_counts, trans_kernel_sizes, trans_layers): diff --git a/codes/models/archs/SRG1_arch.py b/codes/models/archs/SRG1_arch.py index c1af2836..793da965 100644 --- a/codes/models/archs/SRG1_arch.py +++ b/codes/models/archs/SRG1_arch.py @@ -12,9 +12,9 @@ class MultiConvBlock(nn.Module): assert depth >= 2 super(MultiConvBlock, self).__init__() self.noise_scale = nn.Parameter(torch.full((1,), fill_value=.01)) - self.bnconvs = nn.ModuleList([ConvBnLelu(filters_in, filters_mid, kernel_size, bn=bn, bias=False)] + - [ConvBnLelu(filters_mid, filters_mid, kernel_size, bn=bn, bias=False) for i in range(depth-2)] + - [ConvBnLelu(filters_mid, filters_out, kernel_size, lelu=False, bn=False, bias=False)]) + self.bnconvs = nn.ModuleList([ConvBnLelu(filters_in, filters_mid, kernel_size, norm=bn, bias=False)] + + [ConvBnLelu(filters_mid, filters_mid, kernel_size, norm=bn, bias=False) for i in range(depth - 2)] + + [ConvBnLelu(filters_mid, filters_out, kernel_size, activation=False, norm=False, bias=False)]) self.scale = nn.Parameter(torch.full((1,), fill_value=scale_init)) self.bias = nn.Parameter(torch.zeros(1)) @@ -32,8 +32,8 @@ class MultiConvBlock(nn.Module): class HalvingProcessingBlock(nn.Module): def __init__(self, filters): super(HalvingProcessingBlock, self).__init__() - self.bnconv1 = ConvBnSilu(filters, filters * 2, stride=2, bn=False, bias=False) - self.bnconv2 = ConvBnSilu(filters * 2, filters * 2, bn=True, bias=False) + self.bnconv1 = ConvBnSilu(filters, filters * 2, stride=2, norm=False, bias=False) + self.bnconv2 = ConvBnSilu(filters * 2, filters * 2, norm=True, bias=False) def forward(self, x): x = self.bnconv1(x) return self.bnconv2(x) @@ -45,7 +45,7 @@ def create_sequential_growing_processing_block(filters_init, filter_growth, num_ convs = [] current_filters = filters_init for i in range(num_convs): - convs.append(ConvBnSilu(current_filters, current_filters + filter_growth, bn=True, bias=False)) + convs.append(ConvBnSilu(current_filters, current_filters + filter_growth, norm=True, bias=False)) current_filters += filter_growth return nn.Sequential(*convs), current_filters @@ -60,7 +60,7 @@ class SwitchComputer(nn.Module): self.reduction_blocks = nn.ModuleList([HalvingProcessingBlock(filters * 2 ** i) for i in range(reduction_blocks)]) final_filters = filters * 2 ** reduction_blocks self.processing_blocks, final_filters = create_sequential_growing_processing_block(final_filters, growth, processing_blocks) - self.post_interpolate_decimate = ConvBnSilu(final_filters, filters, kernel_size=1, silu=False, bn=False) + self.post_interpolate_decimate = ConvBnSilu(final_filters, filters, kernel_size=1, activation=False, norm=False) self.interpolate_process = ConvBnSilu(filters, filters) self.interpolate_process2 = ConvBnSilu(filters, filters) tc = transform_count diff --git a/codes/models/archs/SwitchedResidualGenerator_arch.py b/codes/models/archs/SwitchedResidualGenerator_arch.py index 8c89fd36..d3253d3f 100644 --- a/codes/models/archs/SwitchedResidualGenerator_arch.py +++ b/codes/models/archs/SwitchedResidualGenerator_arch.py @@ -4,7 +4,7 @@ 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 ConvBnLelu, ConvGnSilu +from models.archs.arch_util import ConvBnLelu, ConvGnSilu, ExpansionBlock from models.archs.RRDBNet_arch import ResidualDenseBlock_5C from models.archs.spinenet_arch import SpineNet from switched_conv_util import save_attention_to_image @@ -15,9 +15,9 @@ class MultiConvBlock(nn.Module): assert depth >= 2 super(MultiConvBlock, self).__init__() self.noise_scale = nn.Parameter(torch.full((1,), fill_value=.01)) - self.bnconvs = nn.ModuleList([ConvBnLelu(filters_in, filters_mid, kernel_size, bn=bn, bias=False, weight_init_factor=weight_init_factor)] + - [ConvBnLelu(filters_mid, filters_mid, kernel_size, bn=bn, bias=False, weight_init_factor=weight_init_factor) for i in range(depth-2)] + - [ConvBnLelu(filters_mid, filters_out, kernel_size, lelu=False, bn=False, bias=False, weight_init_factor=weight_init_factor)]) + self.bnconvs = nn.ModuleList([ConvBnLelu(filters_in, filters_mid, kernel_size, norm=bn, bias=False, weight_init_factor=weight_init_factor)] + + [ConvBnLelu(filters_mid, filters_mid, kernel_size, norm=bn, bias=False, weight_init_factor=weight_init_factor) for i in range(depth - 2)] + + [ConvBnLelu(filters_mid, filters_out, kernel_size, activation=False, norm=False, bias=False, weight_init_factor=weight_init_factor)]) self.scale = nn.Parameter(torch.full((1,), fill_value=scale_init)) self.bias = nn.Parameter(torch.zeros(1)) @@ -35,28 +35,14 @@ class MultiConvBlock(nn.Module): class HalvingProcessingBlock(nn.Module): def __init__(self, filters): super(HalvingProcessingBlock, self).__init__() - self.bnconv1 = ConvGnSilu(filters, filters * 2, stride=2, gn=False, bias=False) - self.bnconv2 = ConvGnSilu(filters * 2, filters * 2, gn=True, bias=False) + self.bnconv1 = ConvGnSilu(filters, filters * 2, stride=2, norm=False, bias=False) + self.bnconv2 = ConvGnSilu(filters * 2, filters * 2, norm=True, bias=False) def forward(self, x): x = self.bnconv1(x) return self.bnconv2(x) -class ExpansionBlock(nn.Module): - def __init__(self, filters): - super(ExpansionBlock, self).__init__() - self.decimate = ConvGnSilu(filters, filters // 2, kernel_size=1, bias=False, silu=False, gn=False) - self.conjoin = ConvGnSilu(filters, filters // 2, kernel_size=3, bias=True, silu=False, gn=True) - self.process = ConvGnSilu(filters // 2, filters // 2, kernel_size=3, bias=False, silu=True, gn=True) - - def forward(self, input, passthrough): - x = F.interpolate(input, scale_factor=2, mode="nearest") - x = self.decimate(x) - x = self.conjoin(torch.cat([x, passthrough], dim=1)) - return self.process(x) - - # This is a classic u-net architecture with the goal of assigning each individual pixel an individual transform # switching set. class ConvBasisMultiplexer(nn.Module): @@ -70,10 +56,10 @@ class ConvBasisMultiplexer(nn.Module): gap = base_filters - multiplexer_channels cbl1_out = ((base_filters - (gap // 2)) // 4) * 4 # Must be multiples of 4 to use with group norm. - self.cbl1 = ConvGnSilu(base_filters, cbl1_out, gn=use_gn, bias=False, num_groups=4) + self.cbl1 = ConvGnSilu(base_filters, cbl1_out, norm=use_gn, bias=False, num_groups=4) cbl2_out = ((base_filters - (3 * gap // 4)) // 4) * 4 - self.cbl2 = ConvGnSilu(cbl1_out, cbl2_out, gn=use_gn, bias=False, num_groups=4) - self.cbl3 = ConvGnSilu(cbl2_out, multiplexer_channels, bias=True, gn=False) + self.cbl2 = ConvGnSilu(cbl1_out, cbl2_out, norm=use_gn, bias=False, num_groups=4) + self.cbl3 = ConvGnSilu(cbl2_out, multiplexer_channels, bias=True, norm=False) def forward(self, x): x = self.filter_conv(x) @@ -109,11 +95,11 @@ class BackboneMultiplexer(nn.Module): self.backbone = backbone self.proc = nn.Sequential(ConvGnSilu(256, 256, kernel_size=3, bias=True), ConvGnSilu(256, 256, kernel_size=3, bias=False)) - self.up1 = nn.Sequential(ConvGnSilu(256, 128, kernel_size=3, bias=False, gn=False, silu=False), + self.up1 = nn.Sequential(ConvGnSilu(256, 128, kernel_size=3, bias=False, norm=False, activation=False), ConvGnSilu(128, 128, kernel_size=3, bias=False)) - self.up2 = nn.Sequential(ConvGnSilu(128, 64, kernel_size=3, bias=False, gn=False, silu=False), + self.up2 = nn.Sequential(ConvGnSilu(128, 64, kernel_size=3, bias=False, norm=False, activation=False), ConvGnSilu(64, 64, kernel_size=3, bias=False)) - self.final = ConvGnSilu(64, transform_count, bias=False, gn=False, silu=False) + self.final = ConvGnSilu(64, transform_count, bias=False, norm=False, activation=False) def forward(self, x): spine = self.backbone.get_forward_result() @@ -139,7 +125,7 @@ class ConfigurableSwitchComputer(nn.Module): # And the switch itself, including learned scalars self.switch = BareConvSwitch(initial_temperature=init_temp) self.switch_scale = nn.Parameter(torch.full((1,), float(1))) - self.post_switch_conv = ConvBnLelu(base_filters, base_filters, bn=False, bias=True) + 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) # depending on its needs. self.psc_scale = nn.Parameter(torch.full((1,), float(.1))) @@ -174,11 +160,11 @@ class ConfigurableSwitchedResidualGenerator2(nn.Module): add_scalable_noise_to_transforms=False): super(ConfigurableSwitchedResidualGenerator2, self).__init__() switches = [] - self.initial_conv = ConvBnLelu(3, transformation_filters, bn=False, lelu=False, bias=True) - self.upconv1 = ConvBnLelu(transformation_filters, transformation_filters, bn=False, bias=True) - self.upconv2 = ConvBnLelu(transformation_filters, transformation_filters, bn=False, bias=True) - self.hr_conv = ConvBnLelu(transformation_filters, transformation_filters, bn=False, bias=True) - self.final_conv = ConvBnLelu(transformation_filters, 3, bn=False, lelu=False, bias=True) + self.initial_conv = ConvBnLelu(3, transformation_filters, norm=False, activation=False, bias=True) + self.upconv1 = ConvBnLelu(transformation_filters, transformation_filters, norm=False, bias=True) + self.upconv2 = ConvBnLelu(transformation_filters, transformation_filters, norm=False, bias=True) + self.hr_conv = ConvBnLelu(transformation_filters, transformation_filters, norm=False, bias=True) + self.final_conv = ConvBnLelu(transformation_filters, 3, norm=False, activation=False, bias=True) for _ in range(switch_depth): multiplx_fn = functools.partial(ConvBasisMultiplexer, transformation_filters, switch_filters, switch_reductions, switch_processing_layers, trans_counts) pretransform_fn = functools.partial(ConvBnLelu, transformation_filters, transformation_filters, bn=False, bias=False, weight_init_factor=.1) @@ -258,19 +244,19 @@ class ConfigurableSwitchedResidualGenerator3(nn.Module): heightened_temp_min=1, heightened_final_step=50000, upsample_factor=4): super(ConfigurableSwitchedResidualGenerator3, self).__init__() - self.initial_conv = ConvBnLelu(3, base_filters, bn=False, lelu=False, bias=True) - self.sw_conv = ConvBnLelu(base_filters, base_filters, lelu=False, bias=True) - self.upconv1 = ConvBnLelu(base_filters, base_filters, bn=False, bias=True) - self.upconv2 = ConvBnLelu(base_filters, base_filters, bn=False, bias=True) - self.hr_conv = ConvBnLelu(base_filters, base_filters, bn=False, bias=True) - self.final_conv = ConvBnLelu(base_filters, 3, bn=False, lelu=False, bias=True) + self.initial_conv = ConvBnLelu(3, base_filters, norm=False, activation=False, bias=True) + self.sw_conv = ConvBnLelu(base_filters, base_filters, activation=False, bias=True) + self.upconv1 = ConvBnLelu(base_filters, base_filters, norm=False, bias=True) + self.upconv2 = ConvBnLelu(base_filters, base_filters, norm=False, bias=True) + self.hr_conv = ConvBnLelu(base_filters, base_filters, norm=False, bias=True) + self.final_conv = ConvBnLelu(base_filters, 3, norm=False, activation=False, bias=True) self.backbone = SpineNet('49', in_channels=3, use_input_norm=True) for p in self.backbone.parameters(recurse=True): p.requires_grad = False self.backbone_wrapper = CachedBackboneWrapper(self.backbone) multiplx_fn = functools.partial(BackboneMultiplexer, self.backbone_wrapper) - pretransform_fn = functools.partial(nn.Sequential, ConvBnLelu(base_filters, base_filters, kernel_size=3, bn=False, lelu=False, bias=False)) + pretransform_fn = functools.partial(nn.Sequential, ConvBnLelu(base_filters, base_filters, kernel_size=3, norm=False, activation=False, bias=False)) transform_fn = functools.partial(MultiConvBlock, base_filters, int(base_filters * 1.5), base_filters, kernel_size=3, depth=4) self.switch = ConfigurableSwitchComputer(base_filters, multiplx_fn, pretransform_fn, transform_fn, trans_count, init_temp=initial_temp, add_scalable_noise_to_transforms=True, init_scalar=.1) diff --git a/codes/models/archs/arch_util.py b/codes/models/archs/arch_util.py index 3cc7df98..86e10e0f 100644 --- a/codes/models/archs/arch_util.py +++ b/codes/models/archs/arch_util.py @@ -184,16 +184,16 @@ class SiLU(nn.Module): ''' Convenience class with Conv->BN->ReLU. Includes weight initialization and auto-padding for standard kernel sizes. ''' class ConvBnRelu(nn.Module): - def __init__(self, filters_in, filters_out, kernel_size=3, stride=1, relu=True, bn=True, bias=True): + def __init__(self, filters_in, filters_out, kernel_size=3, stride=1, activation=True, norm=True, bias=True): super(ConvBnRelu, self).__init__() padding_map = {1: 0, 3: 1, 5: 2, 7: 3} assert kernel_size in padding_map.keys() self.conv = nn.Conv2d(filters_in, filters_out, kernel_size, stride, padding_map[kernel_size], bias=bias) - if bn: + if norm: self.bn = nn.BatchNorm2d(filters_out) else: self.bn = None - if relu: + if activation: self.relu = nn.ReLU() else: self.relu = None @@ -219,16 +219,16 @@ class ConvBnRelu(nn.Module): ''' Convenience class with Conv->BN->SiLU. Includes weight initialization and auto-padding for standard kernel sizes. ''' class ConvBnSilu(nn.Module): - def __init__(self, filters_in, filters_out, kernel_size=3, stride=1, silu=True, bn=True, bias=True, weight_init_factor=1): + def __init__(self, filters_in, filters_out, kernel_size=3, stride=1, activation=True, norm=True, bias=True, weight_init_factor=1): super(ConvBnSilu, self).__init__() padding_map = {1: 0, 3: 1, 5: 2, 7: 3} assert kernel_size in padding_map.keys() self.conv = nn.Conv2d(filters_in, filters_out, kernel_size, stride, padding_map[kernel_size], bias=bias) - if bn: + if norm: self.bn = nn.BatchNorm2d(filters_out) else: self.bn = None - if silu: + if activation: self.silu = SiLU() else: self.silu = None @@ -257,16 +257,16 @@ class ConvBnSilu(nn.Module): ''' Convenience class with Conv->BN->LeakyReLU. Includes weight initialization and auto-padding for standard kernel sizes. ''' class ConvBnLelu(nn.Module): - def __init__(self, filters_in, filters_out, kernel_size=3, stride=1, lelu=True, bn=True, bias=True, weight_init_factor=1): + def __init__(self, filters_in, filters_out, kernel_size=3, stride=1, activation=True, norm=True, bias=True, weight_init_factor=1): super(ConvBnLelu, self).__init__() padding_map = {1: 0, 3: 1, 5: 2, 7: 3} assert kernel_size in padding_map.keys() self.conv = nn.Conv2d(filters_in, filters_out, kernel_size, stride, padding_map[kernel_size], bias=bias) - if bn: + if norm: self.bn = nn.BatchNorm2d(filters_out) else: self.bn = None - if lelu: + if activation: self.lelu = nn.LeakyReLU(negative_slope=.1) else: self.lelu = None @@ -296,16 +296,16 @@ class ConvBnLelu(nn.Module): ''' Convenience class with Conv->GroupNorm->LeakyReLU. Includes weight initialization and auto-padding for standard kernel sizes. ''' class ConvGnLelu(nn.Module): - def __init__(self, filters_in, filters_out, kernel_size=3, stride=1, lelu=True, gn=True, bias=True, num_groups=8): + def __init__(self, filters_in, filters_out, kernel_size=3, stride=1, activation=True, norm=True, bias=True, num_groups=8): super(ConvGnLelu, self).__init__() padding_map = {1: 0, 3: 1, 5: 2, 7: 3} assert kernel_size in padding_map.keys() self.conv = nn.Conv2d(filters_in, filters_out, kernel_size, stride, padding_map[kernel_size], bias=bias) - if gn: + if norm: self.gn = nn.GroupNorm(num_groups, filters_out) else: self.gn = None - if lelu: + if activation: self.lelu = nn.LeakyReLU(negative_slope=.1) else: self.lelu = None @@ -331,16 +331,16 @@ class ConvGnLelu(nn.Module): ''' Convenience class with Conv->BN->SiLU. Includes weight initialization and auto-padding for standard kernel sizes. ''' class ConvGnSilu(nn.Module): - def __init__(self, filters_in, filters_out, kernel_size=3, stride=1, silu=True, gn=True, bias=True, num_groups=8, weight_init_factor=1): + def __init__(self, filters_in, filters_out, kernel_size=3, stride=1, activation=True, norm=True, bias=True, num_groups=8, weight_init_factor=1): super(ConvGnSilu, self).__init__() padding_map = {1: 0, 3: 1, 5: 2, 7: 3} assert kernel_size in padding_map.keys() self.conv = nn.Conv2d(filters_in, filters_out, kernel_size, stride, padding_map[kernel_size], bias=bias) - if gn: + if norm: self.gn = nn.GroupNorm(num_groups, filters_out) else: self.gn = None - if silu: + if activation: self.silu = SiLU() else: self.silu = None @@ -363,4 +363,24 @@ class ConvGnSilu(nn.Module): if self.silu: return self.silu(x) else: - return x \ No newline at end of file + return x + +# Block that upsamples 2x and reduces incoming filters by 2x. It preserves structure by taking a passthrough feed +# along with the feature representation. +class ExpansionBlock(nn.Module): + def __init__(self, filters, block=ConvGnSilu): + super(ExpansionBlock, self).__init__() + self.decimate = block(filters, filters // 2, kernel_size=1, bias=False, activation=False, norm=True) + self.process_passthrough = block(filters // 2, filters // 2, kernel_size=3, bias=True, activation=False, norm=True) + self.conjoin = block(filters, filters // 2, kernel_size=3, bias=False, activation=True, norm=False) + self.process = block(filters // 2, filters // 2, 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.process(x) \ No newline at end of file diff --git a/codes/models/archs/discriminator_vgg_arch.py b/codes/models/archs/discriminator_vgg_arch.py index 61002b59..ddd38a05 100644 --- a/codes/models/archs/discriminator_vgg_arch.py +++ b/codes/models/archs/discriminator_vgg_arch.py @@ -108,20 +108,20 @@ class Discriminator_VGG_PixLoss(nn.Module): self.bn4_1 = nn.GroupNorm(8, nf * 8, affine=True) self.reduce_1 = ConvGnLelu(nf * 8, nf * 4, bias=False) - self.pix_loss_collapse = ConvGnLelu(nf * 4, 1, bias=False, gn=False, lelu=False) + self.pix_loss_collapse = ConvGnLelu(nf * 4, 1, bias=False, norm=False, activation=False) # Pyramid network: upsample with residuals and produce losses at multiple resolutions. - self.up3_decimate = ConvGnLelu(nf * 8, nf * 8, kernel_size=3, bias=True, lelu=False) + self.up3_decimate = ConvGnLelu(nf * 8, nf * 8, kernel_size=3, bias=True, activation=False) self.up3_converge = ConvGnLelu(nf * 16, nf * 8, kernel_size=3, bias=False) self.up3_proc = ConvGnLelu(nf * 8, nf * 8, bias=False) self.up3_reduce = ConvGnLelu(nf * 8, nf * 4, bias=False) - self.up3_pix = ConvGnLelu(nf * 4, 1, bias=False, gn=False, lelu=False) + self.up3_pix = ConvGnLelu(nf * 4, 1, bias=False, norm=False, activation=False) - self.up2_decimate = ConvGnLelu(nf * 8, nf * 4, kernel_size=1, bias=True, lelu=False) + self.up2_decimate = ConvGnLelu(nf * 8, nf * 4, kernel_size=1, bias=True, activation=False) self.up2_converge = ConvGnLelu(nf * 8, nf * 4, kernel_size=3, bias=False) self.up2_proc = ConvGnLelu(nf * 4, nf * 4, bias=False) self.up2_reduce = ConvGnLelu(nf * 4, nf * 2, bias=False) - self.up2_pix = ConvGnLelu(nf * 2, 1, bias=False, gn=False, lelu=False) + self.up2_pix = ConvGnLelu(nf * 2, 1, bias=False, norm=False, activation=False) # activation function self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)