import torch from torch import nn from switched_conv import BareConvSwitch, compute_attention_specificity, AttentionNorm import torch.nn.functional as F import functools from collections import OrderedDict 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 class MultiConvBlock(nn.Module): def __init__(self, filters_in, filters_mid, filters_out, kernel_size, depth, scale_init=1, norm=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, norm=norm, bias=False, weight_init_factor=weight_init_factor)] + [ConvBnLelu(filters_mid, filters_mid, kernel_size, norm=norm, 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)) def forward(self, x, noise=None): if noise is not None: noise = noise * self.noise_scale x = x + noise for m in self.bnconvs: x = m.forward(x) return x * self.scale + self.bias # VGG-style layer with Conv(stride2)->BN->Activation->Conv->BN->Activation # Doubles the input filter count. class HalvingProcessingBlock(nn.Module): def __init__(self, filters): super(HalvingProcessingBlock, self).__init__() 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) # 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): 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)]) 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, norm=use_gn, bias=False, num_groups=4) cbl2_out = ((base_filters - (3 * gap // 4)) // 4) * 4 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) 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) return x class CachedBackboneWrapper: def __init__(self, backbone: nn.Module): self.backbone = backbone def __call__(self, *args): self.cache = self.backbone(*args) return self.cache def get_forward_result(self): return self.cache class BackboneMultiplexer(nn.Module): def __init__(self, backbone: CachedBackboneWrapper, transform_count): super(BackboneMultiplexer, self).__init__() 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, norm=False, activation=False), ConvGnSilu(128, 128, kernel_size=3, bias=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, norm=False, activation=False) def forward(self, x): spine = self.backbone.get_forward_result() feat = self.proc(spine[0]) feat = self.up1(F.interpolate(feat, scale_factor=2, mode="nearest")) feat = self.up2(F.interpolate(feat, scale_factor=2, mode="nearest")) return self.final(feat) class ConfigurableSwitchComputer(nn.Module): def __init__(self, base_filters, multiplexer_net, pre_transform_block, transform_block, transform_count, init_temp=20, add_scalable_noise_to_transforms=False): super(ConfigurableSwitchComputer, self).__init__() tc = transform_count self.multiplexer = multiplexer_net(tc) self.pre_transform = pre_transform_block() self.transforms = nn.ModuleList([transform_block() for _ in range(transform_count)]) self.add_noise = add_scalable_noise_to_transforms self.noise_scale = nn.Parameter(torch.full((1,), float(1e-3))) # And the switch itself, including learned scalars self.switch = BareConvSwitch(initial_temperature=init_temp, attention_norm=AttentionNorm(transform_count, accumulator_size=16 * transform_count)) self.switch_scale = nn.Parameter(torch.full((1,), float(1))) 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))) def forward(self, x, output_attention_weights=False): identity = x if self.add_noise: rand_feature = torch.randn_like(x) * self.noise_scale x = x + rand_feature x = self.pre_transform(x) xformed = [t.forward(x) for t in self.transforms] m = self.multiplexer(identity) outputs, attention = self.switch(xformed, m, True) outputs = identity + outputs * self.switch_scale outputs = outputs + self.post_switch_conv(outputs) * self.psc_scale if output_attention_weights: return outputs, attention else: return outputs def set_temperature(self, temp): self.switch.set_attention_temperature(temp) class ConfigurableSwitchedResidualGenerator2(nn.Module): 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, add_scalable_noise_to_transforms=False): super(ConfigurableSwitchedResidualGenerator2, self).__init__() switches = [] 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, norm=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=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 self.init_temperature = initial_temp self.final_temperature_step = final_temperature_step self.heightened_temp_min = heightened_temp_min self.heightened_final_step = heightened_final_step self.attentions = None self.upsample_factor = upsample_factor assert self.upsample_factor == 2 or self.upsample_factor == 4 def forward(self, x): x = self.initial_conv(x) self.attentions = [] for i, sw in enumerate(self.switches): x, att = sw.forward(x, True) self.attentions.append(att) x = self.upconv1(F.interpolate(x, scale_factor=2, mode="nearest")) if self.upsample_factor > 2: x = F.interpolate(x, scale_factor=2, mode="nearest") x = self.upconv2(x) return self.final_conv(self.hr_conv(x)), 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, int(self.init_temperature * (self.final_temperature_step - step) / self.final_temperature_step)) if temp == 1 and self.heightened_final_step and self.heightened_final_step != 1: # Once the temperature passes (1) it enters an inverted curve to match the linear curve from above. # without this, the attention specificity "spikes" incredibly fast in the last few iterations. h_steps_total = self.heightened_final_step - self.final_temperature_step h_steps_current = max(min(step - self.final_temperature_step, h_steps_total), 1) # The "gap" will represent the steps that need to be traveled as a linear function. h_gap = 1 / self.heightened_temp_min temp = h_gap * h_steps_current / h_steps_total # Invert temperature to represent reality on this side of the curve temp = 1 / temp self.set_temperature(temp) if step % 50 == 0: [save_attention_to_image(experiments_path, self.attentions[i], self.transformation_counts, step, "a%i" % (i+1,)) for i in range(len(self.switches))] 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 class Interpolate(nn.Module): def __init__(self, factor): super(Interpolate, self).__init__() self.factor = factor def forward(self, x): return F.interpolate(x, scale_factor=self.factor) class ConfigurableSwitchedResidualGenerator3(nn.Module): def __init__(self, base_filters, trans_count, initial_temp=20, final_temperature_step=50000, heightened_temp_min=1, heightened_final_step=50000, upsample_factor=4): super(ConfigurableSwitchedResidualGenerator3, self).__init__() 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, 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) self.transformation_counts = trans_count self.init_temperature = initial_temp self.final_temperature_step = final_temperature_step self.heightened_temp_min = heightened_temp_min self.heightened_final_step = heightened_final_step self.attentions = None self.upsample_factor = upsample_factor self.backbone_forward = None def get_forward_results(self): return self.backbone_forward def forward(self, x): self.backbone_forward = self.backbone_wrapper(F.interpolate(x, scale_factor=2, mode="nearest")) x = self.initial_conv(x) self.attentions = [] x, att = self.switch(x, output_attention_weights=True) self.attentions.append(att) x = self.upconv1(F.interpolate(x, scale_factor=2, mode="nearest")) if self.upsample_factor > 2: x = F.interpolate(x, scale_factor=2, mode="nearest") x = self.upconv2(x) return self.final_conv(self.hr_conv(x)), def set_temperature(self, temp): self.switch.set_temperature(temp) def update_for_step(self, step, experiments_path='.'): if self.attentions: temp = max(1, int( self.init_temperature * (self.final_temperature_step - step) / self.final_temperature_step)) if temp == 1 and self.heightened_final_step and self.heightened_final_step != 1: # Once the temperature passes (1) it enters an inverted curve to match the linear curve from above. # without this, the attention specificity "spikes" incredibly fast in the last few iterations. h_steps_total = self.heightened_final_step - self.final_temperature_step h_steps_current = min(step - self.final_temperature_step, h_steps_total) # The "gap" will represent the steps that need to be traveled as a linear function. h_gap = 1 / self.heightened_temp_min temp = h_gap * h_steps_current / h_steps_total # Invert temperature to represent reality on this side of the curve temp = 1 / temp self.set_temperature(temp) if step % 50 == 0: save_attention_to_image(experiments_path, self.attentions[0], self.transformation_counts, step, "a%i" % (1,), l_mult=10) def get_debug_values(self, step): temp = self.switch.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