diff --git a/codes/models/archs/NestedSwitchGenerator.py b/codes/models/archs/NestedSwitchGenerator.py index f663f4cc..ec2d4dc8 100644 --- a/codes/models/archs/NestedSwitchGenerator.py +++ b/codes/models/archs/NestedSwitchGenerator.py @@ -1,6 +1,7 @@ import torch from torch import nn -from models.archs.SwitchedResidualGenerator_arch import ConvBnLelu, ConvBnRelu, MultiConvBlock, initialize_weights +from models.archs.arch_util import ConvBnLelu, ConvBnRelu +from models.archs.SwitchedResidualGenerator_arch import MultiConvBlock from switched_conv import BareConvSwitch, compute_attention_specificity from switched_conv_util import save_attention_to_image from functools import partial diff --git a/codes/models/archs/SwitchedResidualGenerator_arch.py b/codes/models/archs/SwitchedResidualGenerator_arch.py index 3ebb6ea5..ec0f4fe1 100644 --- a/codes/models/archs/SwitchedResidualGenerator_arch.py +++ b/codes/models/archs/SwitchedResidualGenerator_arch.py @@ -4,20 +4,20 @@ 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, ConvBnRelu, ConvBnLelu, ConvBnSilu +from models.archs.arch_util import ConvBnLelu, ConvGnSilu from models.archs.RRDBNet_arch import ResidualDenseBlock_5C from models.archs.spinenet_arch import SpineNet from switched_conv_util import save_attention_to_image class MultiConvBlock(nn.Module): - def __init__(self, filters_in, filters_mid, filters_out, kernel_size, depth, scale_init=1, bn=False): + def __init__(self, filters_in, filters_mid, filters_out, kernel_size, depth, scale_init=1, bn=False, weight_init_factor=1): 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, 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.scale = nn.Parameter(torch.full((1,), fill_value=scale_init)) self.bias = nn.Parameter(torch.zeros(1)) @@ -35,43 +35,56 @@ class MultiConvBlock(nn.Module): class HalvingProcessingBlock(nn.Module): def __init__(self, filters): super(HalvingProcessingBlock, self).__init__() - self.bnconv1 = ConvBnLelu(filters, filters * 2, stride=2, bn=False, bias=False) - self.bnconv2 = ConvBnLelu(filters * 2, filters * 2, bn=True, bias=False) + self.bnconv1 = ConvGnSilu(filters, filters * 2, stride=2, gn=False, bias=False) + self.bnconv2 = ConvGnSilu(filters * 2, filters * 2, gn=True, bias=False) def forward(self, x): x = self.bnconv1(x) 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(ConvBnSilu(current_filters, current_filters + filter_growth, bn=True, bias=False)) - current_filters += filter_growth - return nn.Sequential(*convs), current_filters +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): - def __init__(self, input_channels, base_filters, growth, reductions, processing_depth, multiplexer_channels, use_bn=True): + def __init__(self, input_channels, base_filters, reductions, processing_depth, multiplexer_channels, use_gn=True): super(ConvBasisMultiplexer, self).__init__() - self.filter_conv = ConvBnSilu(input_channels, base_filters, bias=True) - self.reduction_blocks = nn.Sequential(OrderedDict([('block%i:' % (i,), HalvingProcessingBlock(base_filters * 2 ** i)) for i in range(reductions)])) + 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, self.output_filter_count = create_sequential_growing_processing_block(reduction_filters, growth, processing_depth) + 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)]) - gap = self.output_filter_count - multiplexer_channels - # Hey silly - if you're going to interpolate later, do it here instead. Then add some processing layers to let the model adjust it properly. - self.cbl1 = ConvBnSilu(self.output_filter_count, self.output_filter_count - (gap // 2), bn=use_bn, bias=False) - self.cbl2 = ConvBnSilu(self.output_filter_count - (gap // 2), self.output_filter_count - (3 * gap // 4), bn=use_bn, bias=False) - self.cbl3 = ConvBnSilu(self.output_filter_count - (3 * gap // 4), multiplexer_channels, bias=True) + 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) + 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) def forward(self, x): x = self.filter_conv(x) - x = self.reduction_blocks(x) + reduction_identities = [] + for b in self.reduction_blocks: + reduction_identities.append(x) + x = b(x) x = self.processing_blocks(x) + for i, b in enumerate(self.expansion_blocks): + x = b(x, reduction_identities[-i - 1]) + x = self.cbl1(x) x = self.cbl2(x) x = self.cbl3(x) @@ -94,13 +107,13 @@ class BackboneMultiplexer(nn.Module): def __init__(self, backbone: CachedBackboneWrapper, transform_count): super(BackboneMultiplexer, self).__init__() self.backbone = backbone - self.proc = nn.Sequential(ConvBnSilu(256, 256, kernel_size=3, bias=True), - ConvBnSilu(256, 256, kernel_size=3, bias=False)) - self.up1 = nn.Sequential(ConvBnSilu(256, 128, kernel_size=3, bias=False, bn=False, silu=False), - ConvBnSilu(128, 128, kernel_size=3, bias=False)) - self.up2 = nn.Sequential(ConvBnSilu(128, 64, kernel_size=3, bias=False, bn=False, silu=False), - ConvBnSilu(64, 64, kernel_size=3, bias=False)) - self.final = ConvBnSilu(64, transform_count, bias=False, bn=False, silu=False) + 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), + ConvGnSilu(128, 128, kernel_size=3, bias=False)) + self.up2 = nn.Sequential(ConvGnSilu(128, 64, kernel_size=3, bias=False, gn=False, silu=False), + ConvGnSilu(64, 64, kernel_size=3, bias=False)) + self.final = ConvGnSilu(64, transform_count, bias=False, gn=False, silu=False) def forward(self, x): spine = self.backbone.get_forward_result() @@ -112,13 +125,10 @@ class BackboneMultiplexer(nn.Module): class ConfigurableSwitchComputer(nn.Module): def __init__(self, base_filters, multiplexer_net, pre_transform_block, transform_block, transform_count, init_temp=20, - enable_negative_transforms=False, add_scalable_noise_to_transforms=False, init_scalar=1): + add_scalable_noise_to_transforms=False): super(ConfigurableSwitchComputer, self).__init__() - self.enable_negative_transforms = enable_negative_transforms tc = transform_count - if self.enable_negative_transforms: - tc = transform_count * 2 self.multiplexer = multiplexer_net(tc) self.pre_transform = pre_transform_block() @@ -128,11 +138,11 @@ 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(init_scalar))) - self.post_switch_conv = ConvBnLelu(base_filters, base_filters, bn=False, bias=False) - # The post_switch_conv gets a near-zero scale. The network can decide to magnify it (or not) depending on its needs. - self.psc_scale = nn.Parameter(torch.full((1,), float(1e-3))) - self.bias = nn.Parameter(torch.zeros(1)) + self.switch_scale = nn.Parameter(torch.full((1,), float(1))) + self.post_switch_conv = ConvBnLelu(base_filters, base_filters, bn=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))) def forward(self, x, output_attention_weights=False): identity = x @@ -142,17 +152,12 @@ class ConfigurableSwitchComputer(nn.Module): x = self.pre_transform(x) xformed = [t.forward(x) for t in self.transforms] - if self.enable_negative_transforms: - xformed.extend([-t for t in xformed]) m = self.multiplexer(identity) - # Interpolate the multiplexer across the entire shape of the image. - m = F.interpolate(m, size=xformed[0].shape[2:], mode='nearest') outputs, attention = self.switch(xformed, m, True) outputs = identity + outputs * self.switch_scale - outputs = identity + self.post_switch_conv(outputs) * self.psc_scale - outputs = outputs + self.bias + outputs = outputs + self.post_switch_conv(outputs) * self.psc_scale if output_attention_weights: return outputs, attention else: @@ -163,25 +168,25 @@ class ConfigurableSwitchComputer(nn.Module): class ConfigurableSwitchedResidualGenerator2(nn.Module): - def __init__(self, switch_filters, switch_growths, switch_reductions, switch_processing_layers, trans_counts, trans_kernel_sizes, + def __init__(self, switch_depth, switch_filters, switch_reductions, switch_processing_layers, trans_counts, trans_kernel_sizes, trans_layers, transformation_filters, initial_temp=20, final_temperature_step=50000, heightened_temp_min=1, - heightened_final_step=50000, upsample_factor=1, enable_negative_transforms=False, + heightened_final_step=50000, upsample_factor=1, 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.sw_conv = ConvBnLelu(transformation_filters, transformation_filters, 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) - for filters, growth, sw_reduce, sw_proc, trans_count, kernel, layers in zip(switch_filters, switch_growths, switch_reductions, switch_processing_layers, trans_counts, trans_kernel_sizes, trans_layers): - multiplx_fn = functools.partial(ConvBasisMultiplexer, transformation_filters, filters, growth, sw_reduce, sw_proc, trans_count) + 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) + transform_fn = functools.partial(MultiConvBlock, transformation_filters, int(transformation_filters * 1.5), transformation_filters, kernel_size=trans_kernel_sizes, depth=trans_layers, weight_init_factor=.1) switches.append(ConfigurableSwitchComputer(transformation_filters, multiplx_fn, - pre_transform_block=functools.partial(ConvBnLelu, transformation_filters, transformation_filters, bn=False, bias=False), - transform_block=functools.partial(MultiConvBlock, transformation_filters, transformation_filters + growth, transformation_filters, kernel_size=kernel, depth=layers), - transform_count=trans_count, init_temp=initial_temp, enable_negative_transforms=enable_negative_transforms, - add_scalable_noise_to_transforms=add_scalable_noise_to_transforms, init_scalar=.1)) + pre_transform_block=pretransform_fn, transform_block=transform_fn, + transform_count=trans_counts, init_temp=initial_temp, + add_scalable_noise_to_transforms=add_scalable_noise_to_transforms)) self.switches = nn.ModuleList(switches) self.transformation_counts = trans_counts @@ -268,7 +273,7 @@ class ConfigurableSwitchedResidualGenerator3(nn.Module): pretransform_fn = functools.partial(nn.Sequential, ConvBnLelu(base_filters, base_filters, kernel_size=3, bn=False, lelu=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, - enable_negative_transforms=False, add_scalable_noise_to_transforms=True, init_scalar=.1) + add_scalable_noise_to_transforms=True, init_scalar=.1) self.transformation_counts = trans_count self.init_temperature = initial_temp diff --git a/codes/models/archs/arch_util.py b/codes/models/archs/arch_util.py index afb99703..3cc7df98 100644 --- a/codes/models/archs/arch_util.py +++ b/codes/models/archs/arch_util.py @@ -219,7 +219,7 @@ 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): + def __init__(self, filters_in, filters_out, kernel_size=3, stride=1, silu=True, bn=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() @@ -237,6 +237,9 @@ class ConvBnSilu(nn.Module): for m in self.modules(): if isinstance(m, nn.Conv2d): nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu' if self.silu else 'linear') + m.weight.data *= weight_init_factor + if m.bias is not None: + m.bias.data.zero_() elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)): nn.init.constant_(m.weight, 1) nn.init.constant_(m.bias, 0) @@ -254,7 +257,7 @@ 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): + def __init__(self, filters_in, filters_out, kernel_size=3, stride=1, lelu=True, bn=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() @@ -273,6 +276,9 @@ class ConvBnLelu(nn.Module): if isinstance(m, nn.Conv2d): nn.init.kaiming_normal_(m.weight, a=.1, mode='fan_out', nonlinearity='leaky_relu' if self.lelu else 'linear') + m.weight.data *= weight_init_factor + if m.bias is not None: + m.bias.data.zero_() elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)): nn.init.constant_(m.weight, 1) nn.init.constant_(m.bias, 0) @@ -319,5 +325,42 @@ class ConvGnLelu(nn.Module): x = self.gn(x) if self.lelu: return self.lelu(x) + else: + return x + +''' 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): + 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: + self.gn = nn.GroupNorm(num_groups, filters_out) + else: + self.gn = None + if silu: + self.silu = SiLU() + else: + self.silu = None + + # Init params. + for m in self.modules(): + if isinstance(m, nn.Conv2d): + nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu' if self.silu else 'linear') + m.weight.data *= weight_init_factor + if m.bias is not None: + m.bias.data.zero_() + elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)): + nn.init.constant_(m.weight, 1) + nn.init.constant_(m.bias, 0) + + def forward(self, x): + x = self.conv(x) + if self.gn: + x = self.gn(x) + if self.silu: + return self.silu(x) else: return x \ No newline at end of file diff --git a/codes/models/networks.py b/codes/models/networks.py index 23270e72..3495dd16 100644 --- a/codes/models/networks.py +++ b/codes/models/networks.py @@ -59,7 +59,7 @@ def define_G(opt, net_key='network_G'): heightened_temp_min=opt_net['heightened_temp_min'], heightened_final_step=opt_net['heightened_final_step'], upsample_factor=scale, add_scalable_noise_to_transforms=opt_net['add_noise']) elif which_model == "ConfigurableSwitchedResidualGenerator2": - netG = SwitchedGen_arch.ConfigurableSwitchedResidualGenerator2(switch_filters=opt_net['switch_filters'], switch_growths=opt_net['switch_growths'], + netG = SwitchedGen_arch.ConfigurableSwitchedResidualGenerator2(switch_depth=opt_net['switch_depth'], switch_filters=opt_net['switch_filters'], 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'], diff --git a/codes/train.py b/codes/train.py index 6ad0f763..9259c31a 100644 --- a/codes/train.py +++ b/codes/train.py @@ -32,7 +32,7 @@ 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_div2k_pixgan_rrdb.yml') + parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/train_div2k_pixgan_srg2.yml') parser.add_argument('--launcher', choices=['none', 'pytorch'], default='none', help='job launcher') parser.add_argument('--local_rank', type=int, default=0) diff --git a/codes/utils/numeric_stability.py b/codes/utils/numeric_stability.py index 28b3c0d6..4aa40ba9 100644 --- a/codes/utils/numeric_stability.py +++ b/codes/utils/numeric_stability.py @@ -97,20 +97,19 @@ if __name__ == "__main__": torch.randn(1, 3, 64, 64), device='cuda') ''' - ''' test_stability(functools.partial(srg.ConfigurableSwitchedResidualGenerator2, - switch_filters=[32,32,32,32], - switch_growths=[16,16,16,16], - switch_reductions=[4,3,2,1], - switch_processing_layers=[3,3,4,5], - trans_counts=[16,16,16,16,16], - trans_kernel_sizes=[3,3,3,3,3], - trans_layers=[3,3,3,3,3], + switch_depth=4, + switch_filters=64, + switch_reductions=4, + switch_processing_layers=2, + trans_counts=8, + trans_kernel_sizes=3, + trans_layers=4, transformation_filters=64, - initial_temp=10), + upsample_factor=4), torch.randn(1, 3, 64, 64), device='cuda') - ''' + ''' test_stability(functools.partial(srg1.ConfigurableSwitchedResidualGenerator, switch_filters=[32,32,32,32], @@ -125,7 +124,9 @@ if __name__ == "__main__": torch.randn(1, 3, 64, 64), device='cuda') ''' + ''' test_stability(functools.partial(srg.ConfigurableSwitchedResidualGenerator3, 64, 16), torch.randn(1, 3, 64, 64), - device='cuda') \ No newline at end of file + device='cuda') + ''' \ No newline at end of file