import math import functools from models.archs.arch_util import MultiConvBlock, ConvGnLelu, ConvGnSilu, ReferenceJoinBlock from models.archs.SwitchedResidualGenerator_arch import ConfigurableSwitchComputer from models.archs.SPSR_arch import ImageGradientNoPadding from torch import nn import torch import torch.nn.functional as F from switched_conv_util import save_attention_to_image_rgb from switched_conv import compute_attention_specificity import os import torchvision # 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, kernel_size=1, 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 ExpansionBlock2(nn.Module): def __init__(self, filters_in, filters_out=None, block=ConvGnSilu): super(ExpansionBlock2, self).__init__() if filters_out is None: filters_out = filters_in // 2 self.decimate = block(filters_in, filters_out, kernel_size=1, bias=False, activation=True, norm=False) self.process_passthrough = block(filters_out, filters_out, kernel_size=3, bias=True, activation=True, norm=False) self.conjoin = block(filters_out*2, filters_out*2, kernel_size=1, bias=False, activation=True, norm=False) self.reduce = block(filters_out*2, filters_out, kernel_size=1, bias=False, activation=False, 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.reduce(x) # Basic convolutional upsampling block that uses interpolate. class UpconvBlock(nn.Module): def __init__(self, filters_in, filters_out=None, block=ConvGnSilu, norm=True, activation=True, bias=False): super(UpconvBlock, self).__init__() self.reduce = block(filters_in, filters_out, kernel_size=1, bias=False, activation=False, norm=False) self.process = block(filters_out, filters_out, kernel_size=3, bias=bias, activation=activation, norm=norm) def forward(self, x): x = self.reduce(x) x = F.interpolate(x, scale_factor=2, mode="nearest") return self.process(x) class SSGMultiplexer(nn.Module): def __init__(self, nf, multiplexer_channels, reductions=2): super(SSGMultiplexer, self).__init__() # Blocks used to create the query self.input_process = ConvGnSilu(nf, nf, activation=True, norm=False, bias=True) self.embedding_process = ConvGnSilu(256, 256, activation=True, norm=False, bias=True) self.reduction_blocks = nn.ModuleList([HalvingProcessingBlock(nf * 2 ** i) for i in range(reductions)]) reduction_filters = nf * 2 ** reductions self.processing_blocks = nn.Sequential( ConvGnSilu(reduction_filters + 256, reduction_filters + 128, kernel_size=1, activation=True, norm=False, bias=True), ConvGnSilu(reduction_filters + 128, reduction_filters, kernel_size=3, activation=True, norm=True, bias=False)) self.expansion_blocks = nn.ModuleList([ExpansionBlock2(reduction_filters // (2 ** i)) for i in range(reductions)]) # Blocks used to create the key self.key_process = ConvGnSilu(nf, nf, kernel_size=1, activation=True, norm=False, bias=True) # Postprocessing blocks. self.query_key_combine = ConvGnSilu(nf*2, nf, kernel_size=1, activation=True, norm=False, bias=False) self.cbl1 = ConvGnSilu(nf, nf // 4, kernel_size=1, activation=True, norm=True, bias=False, num_groups=4) self.cbl2 = ConvGnSilu(nf // 4, 1, kernel_size=1, activation=False, norm=False, bias=False) def forward(self, x, embedding, transformations): q = self.input_process(x) embedding = self.embedding_process(embedding) reduction_identities = [] for b in self.reduction_blocks: reduction_identities.append(q) q = b(q) q = self.processing_blocks(torch.cat([q, embedding], dim=1)) for i, b in enumerate(self.expansion_blocks): q = b(q, reduction_identities[-i - 1]) b, t, f, h, w = transformations.shape k = transformations.view(b * t, f, h, w) k = self.key_process(k) q = q.view(b, 1, f, h, w).repeat(1, t, 1, 1, 1).view(b * t, f, h, w) v = self.query_key_combine(torch.cat([q, k], dim=1)) v = self.cbl1(v) v = self.cbl2(v) return v.view(b, t, h, w) class SSGr1(nn.Module): def __init__(self, in_nc, out_nc, nf, xforms=8, upscale=4, init_temperature=10): super(SSGr1, self).__init__() n_upscale = int(math.log(upscale, 2)) # switch options transformation_filters = nf self.transformation_counts = xforms multiplx_fn = functools.partial(SSGMultiplexer, transformation_filters) transform_fn = functools.partial(MultiConvBlock, transformation_filters, int(transformation_filters * 1.25), transformation_filters, kernel_size=3, depth=3, weight_init_factor=.1) # Feature branch self.model_fea_conv = ConvGnLelu(in_nc, nf, kernel_size=3, norm=False, activation=False) self.noise_ref_join = ReferenceJoinBlock(nf, residual_weight_init_factor=.1, kernel_size=1, depth=2) self.sw1 = ConfigurableSwitchComputer(transformation_filters, multiplx_fn, pre_transform_block=None, transform_block=transform_fn, attention_norm=True, transform_count=self.transformation_counts, init_temp=init_temperature, add_scalable_noise_to_transforms=False, feed_transforms_into_multiplexer=True) self.feature_lr_conv = ConvGnLelu(nf, nf, kernel_size=3, norm=True, activation=False) self.feature_lr_conv2 = ConvGnLelu(nf, nf, kernel_size=3, norm=False, activation=False, bias=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=3, norm=False, activation=False, bias=False) self.noise_ref_join_grad = ReferenceJoinBlock(nf, residual_weight_init_factor=.1, kernel_size=1, depth=2) self.grad_ref_join = ReferenceJoinBlock(nf, residual_weight_init_factor=.3, final_norm=False, kernel_size=1, depth=2) self.sw_grad = ConfigurableSwitchComputer(transformation_filters, multiplx_fn, pre_transform_block=None, transform_block=transform_fn, attention_norm=True, transform_count=self.transformation_counts // 2, init_temp=init_temperature, add_scalable_noise_to_transforms=False, feed_transforms_into_multiplexer=True) self.grad_lr_conv = ConvGnLelu(nf, nf, kernel_size=3, norm=False, activation=True, bias=True) self.upsample_grad = nn.Sequential(*[UpconvBlock(nf, nf // 2, block=ConvGnLelu, norm=False, activation=True, bias=False) for _ in range(n_upscale)]) self.grad_branch_output_conv = ConvGnLelu(nf // 2, out_nc, kernel_size=1, norm=False, activation=False, bias=True) # Join branch (grad+fea) self.noise_ref_join_conjoin = ReferenceJoinBlock(nf, residual_weight_init_factor=.1, kernel_size=1, depth=2) self.conjoin_ref_join = ReferenceJoinBlock(nf, residual_weight_init_factor=.3, kernel_size=1, depth=2) self.conjoin_sw = ConfigurableSwitchComputer(transformation_filters, multiplx_fn, pre_transform_block=None, transform_block=transform_fn, attention_norm=True, transform_count=self.transformation_counts, init_temp=init_temperature, add_scalable_noise_to_transforms=False, feed_transforms_into_multiplexer=True) self.final_lr_conv = ConvGnLelu(nf, nf, kernel_size=3, norm=False, activation=True, bias=True) self.upsample = nn.Sequential(*[UpconvBlock(nf, 64, block=ConvGnLelu, norm=False, activation=True, bias=True) for _ in range(n_upscale)]) self.final_hr_conv2 = ConvGnLelu(64, out_nc, kernel_size=3, norm=False, activation=False, bias=False) self.switches = [self.sw1, self.sw_grad, self.conjoin_sw] self.attentions = None self.lr = None self.init_temperature = init_temperature self.final_temperature_step = 10000 def forward(self, x, embedding): noise_stds = [] # The attention_maps debugger outputs . Save that here. self.lr = x.detach().cpu() x_grad = self.get_g_nopadding(x) x = self.model_fea_conv(x) x1 = x x1, a1 = self.sw1(x1, True, identity=x, att_in=(x1, embedding)) x_grad = self.grad_conv(x_grad) x_grad_identity = x_grad x_grad, nstd = self.noise_ref_join_grad(x_grad, torch.randn_like(x_grad)) x_grad, grad_fea_std = self.grad_ref_join(x_grad, x1) x_grad, a3 = self.sw_grad(x_grad, True, identity=x_grad_identity, att_in=(x_grad, embedding)) x_grad = self.grad_lr_conv(x_grad) x_grad_out = self.upsample_grad(x_grad) x_grad_out = self.grad_branch_output_conv(x_grad_out) noise_stds.append(nstd) x_out = x1 x_out, nstd = self.noise_ref_join_conjoin(x_out, torch.randn_like(x_out)) x_out, fea_grad_std = self.conjoin_ref_join(x_out, x_grad) x_out, a4 = self.conjoin_sw(x_out, True, identity=x1, att_in=(x_out, embedding)) x_out = self.final_lr_conv(x_out) x_out = self.upsample(x_out) x_out = self.final_hr_conv2(x_out) noise_stds.append(nstd) self.attentions = [a1, a3, a4] self.noise_stds = torch.stack(noise_stds).mean().detach().cpu() self.grad_fea_std = grad_fea_std.detach().cpu() self.fea_grad_std = fea_grad_std.detach().cpu() return x_grad_out, x_out, x_grad 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, 1 + self.init_temperature * (self.final_temperature_step - step) / self.final_temperature_step) self.set_temperature(temp) if step % 200 == 0: output_path = os.path.join(experiments_path, "attention_maps") prefix = "amap_%i_a%i_%%i.png" [save_attention_to_image_rgb(output_path, self.attentions[i], self.transformation_counts, prefix % (step, i), step, output_mag=False) for i in range(len(self.attentions))] torchvision.utils.save_image(self.lr, os.path.join(experiments_path, "attention_maps", "amap_%i_base_image.png" % (step,))) def get_debug_values(self, step, net_name): 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, "noise_branch_std_dev": self.noise_stds, "grad_branch_feat_intg_std_dev": self.grad_fea_std, "conjoin_branch_grad_intg_std_dev": self.fea_grad_std} for i in range(len(means)): val["switch_%i_specificity" % (i,)] = means[i] val["switch_%i_histogram" % (i,)] = hists[i] return val class SSGMultiplexerNoEmbedding(nn.Module): def __init__(self, nf, multiplexer_channels, reductions=2): super(SSGMultiplexerNoEmbedding, self).__init__() # Blocks used to create the query self.input_process = ConvGnSilu(nf, nf, activation=True, norm=False, bias=True) self.reduction_blocks = nn.ModuleList([HalvingProcessingBlock(nf * 2 ** i) for i in range(reductions)]) reduction_filters = nf * 2 ** 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(reduction_filters // (2 ** i)) for i in range(reductions)]) # Blocks used to create the key self.key_process = ConvGnSilu(nf, nf, kernel_size=1, activation=True, norm=False, bias=True) # Postprocessing blocks. self.query_key_combine = ConvGnSilu(nf*2, nf, kernel_size=1, activation=True, norm=False, bias=False) self.cbl1 = ConvGnSilu(nf, nf // 4, kernel_size=1, activation=True, norm=True, bias=False, num_groups=4) self.cbl2 = ConvGnSilu(nf // 4, 1, kernel_size=1, activation=False, norm=False, bias=False) def forward(self, x, transformations): 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]) b, t, f, h, w = transformations.shape k = transformations.view(b * t, f, h, w) k = self.key_process(k) q = q.view(b, 1, f, h, w).repeat(1, t, 1, 1, 1).view(b * t, f, h, w) v = self.query_key_combine(torch.cat([q, k], dim=1)) v = self.cbl1(v) v = self.cbl2(v) return v.view(b, t, h, w) class SSGNoEmbedding(nn.Module): def __init__(self, in_nc, out_nc, nf, xforms=8, upscale=4, init_temperature=10): super(SSGNoEmbedding, self).__init__() n_upscale = int(math.log(upscale, 2)) # switch options transformation_filters = nf self.transformation_counts = xforms multiplx_fn = functools.partial(SSGMultiplexerNoEmbedding, transformation_filters, reductions=3) transform_fn = functools.partial(MultiConvBlock, transformation_filters, int(transformation_filters * 1.25), transformation_filters, kernel_size=3, depth=3, weight_init_factor=.1) # Feature branch self.model_fea_conv = ConvGnLelu(in_nc, nf, kernel_size=3, norm=False, activation=False) self.noise_ref_join = ReferenceJoinBlock(nf, residual_weight_init_factor=.1, kernel_size=1, depth=2) self.sw1 = ConfigurableSwitchComputer(transformation_filters, multiplx_fn, pre_transform_block=None, transform_block=transform_fn, attention_norm=True, transform_count=self.transformation_counts, init_temp=init_temperature, add_scalable_noise_to_transforms=False, feed_transforms_into_multiplexer=True) self.feature_lr_conv = ConvGnLelu(nf, nf, kernel_size=3, norm=True, activation=False) self.feature_lr_conv2 = ConvGnLelu(nf, nf, kernel_size=3, norm=False, activation=False, bias=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=3, norm=False, activation=False, bias=False) self.noise_ref_join_grad = ReferenceJoinBlock(nf, residual_weight_init_factor=.1, kernel_size=1, depth=2) self.grad_ref_join = ReferenceJoinBlock(nf, residual_weight_init_factor=.3, final_norm=False, kernel_size=1, depth=2) self.sw_grad = ConfigurableSwitchComputer(transformation_filters, multiplx_fn, pre_transform_block=None, transform_block=transform_fn, attention_norm=True, transform_count=self.transformation_counts // 2, init_temp=init_temperature, add_scalable_noise_to_transforms=False, feed_transforms_into_multiplexer=True) self.grad_lr_conv = ConvGnLelu(nf, nf, kernel_size=3, norm=False, activation=True, bias=True) self.upsample_grad = nn.Sequential( *[UpconvBlock(nf, nf, block=ConvGnLelu, norm=False, activation=True, bias=False) for _ in range(n_upscale)]) self.grad_branch_output_conv = ConvGnLelu(nf, out_nc, kernel_size=1, norm=False, activation=False, bias=True) # Join branch (grad+fea) self.noise_ref_join_conjoin = ReferenceJoinBlock(nf, residual_weight_init_factor=.1, kernel_size=1, depth=2) self.conjoin_ref_join = ReferenceJoinBlock(nf, residual_weight_init_factor=.3, kernel_size=1, depth=2) self.conjoin_sw = ConfigurableSwitchComputer(transformation_filters, multiplx_fn, pre_transform_block=None, transform_block=transform_fn, attention_norm=True, transform_count=self.transformation_counts, init_temp=init_temperature, add_scalable_noise_to_transforms=False, feed_transforms_into_multiplexer=True) self.final_lr_conv = ConvGnLelu(nf, nf, kernel_size=3, norm=False, activation=True, bias=True) self.upsample = nn.Sequential( *[UpconvBlock(nf, nf, block=ConvGnLelu, norm=False, activation=True, bias=True) for _ in range(n_upscale)]) self.final_hr_conv2 = ConvGnLelu(nf, out_nc, kernel_size=3, norm=False, activation=False, bias=False) self.switches = [self.sw1, self.sw_grad, self.conjoin_sw] self.attentions = None self.lr = None self.init_temperature = init_temperature self.final_temperature_step = 10000 def forward(self, x, *args): noise_stds = [] # The attention_maps debugger outputs . Save that here. self.lr = x.detach().cpu() x_grad = self.get_g_nopadding(x) x = self.model_fea_conv(x) x1, a1 = self.sw1(x, True) x_grad = self.grad_conv(x_grad) x_grad_identity = x_grad x_grad, nstd = self.noise_ref_join_grad(x_grad, torch.randn_like(x_grad)) x_grad, grad_fea_std = self.grad_ref_join(x_grad, x1) x_grad, a3 = self.sw_grad(x_grad, True, identity=x_grad_identity) x_grad = self.grad_lr_conv(x_grad) x_grad_out = self.upsample_grad(x_grad) x_grad_out = self.grad_branch_output_conv(x_grad_out) noise_stds.append(nstd) x_out = x1 x_out, nstd = self.noise_ref_join_conjoin(x_out, torch.randn_like(x_out)) x_out, fea_grad_std = self.conjoin_ref_join(x_out, x_grad) x_out, a4 = self.conjoin_sw(x_out, True, identity=x1) x_out = self.final_lr_conv(x_out) x_out = self.upsample(x_out) x_out = self.final_hr_conv2(x_out) noise_stds.append(nstd) self.attentions = [a1, a3, a4] self.noise_stds = torch.stack(noise_stds).mean().detach().cpu() self.grad_fea_std = grad_fea_std.detach().cpu() self.fea_grad_std = fea_grad_std.detach().cpu() return x_grad_out, x_out, x_grad 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, 1 + self.init_temperature * (self.final_temperature_step - step) / self.final_temperature_step) self.set_temperature(temp) if step % 200 == 0: output_path = os.path.join(experiments_path, "attention_maps") prefix = "amap_%i_a%i_%%i.png" [save_attention_to_image_rgb(output_path, self.attentions[i], self.transformation_counts, prefix % (step, i), step, output_mag=False) for i in range(len(self.attentions))] torchvision.utils.save_image(self.lr, os.path.join(experiments_path, "attention_maps", "amap_%i_base_image.png" % (step,))) def get_debug_values(self, step, net_name): 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, "noise_branch_std_dev": self.noise_stds, "grad_branch_feat_intg_std_dev": self.grad_fea_std, "conjoin_branch_grad_intg_std_dev": self.fea_grad_std} for i in range(len(means)): val["switch_%i_specificity" % (i,)] = means[i] val["switch_%i_histogram" % (i,)] = hists[i] return val class SSGLite(nn.Module): def __init__(self, in_nc, out_nc, nf, xforms=8, upscale=4, init_temperature=10): super(SSGLite, self).__init__() # switch options transformation_filters = nf self.transformation_counts = xforms multiplx_fn = functools.partial(SSGMultiplexerNoEmbedding, transformation_filters, reductions=3) transform_fn = functools.partial(MultiConvBlock, transformation_filters, int(transformation_filters * 1.25), transformation_filters, kernel_size=5, depth=3, weight_init_factor=.1) self.model_fea_conv = ConvGnLelu(in_nc, nf, kernel_size=3, norm=False, activation=False) self.noise_ref_join = ReferenceJoinBlock(nf, residual_weight_init_factor=.1, kernel_size=1, depth=2) self.sw1 = ConfigurableSwitchComputer(transformation_filters, multiplx_fn, pre_transform_block=None, transform_block=transform_fn, attention_norm=True, transform_count=self.transformation_counts, init_temp=init_temperature, add_scalable_noise_to_transforms=False, feed_transforms_into_multiplexer=True) self.intermediate_conv = ConvGnLelu(nf, nf, kernel_size=1, norm=True, activation=False) self.noise_ref_join_conjoin = ReferenceJoinBlock(nf, residual_weight_init_factor=.1, kernel_size=1, depth=2) self.sw2 = ConfigurableSwitchComputer(transformation_filters, multiplx_fn, pre_transform_block=None, transform_block=transform_fn, attention_norm=True, transform_count=self.transformation_counts, init_temp=init_temperature, add_scalable_noise_to_transforms=False, feed_transforms_into_multiplexer=True) self.intermediate_conv2 = ConvGnLelu(nf, nf, kernel_size=1, norm=True, activation=False) self.sw3 = ConfigurableSwitchComputer(transformation_filters, multiplx_fn, pre_transform_block=None, transform_block=transform_fn, attention_norm=True, transform_count=self.transformation_counts, init_temp=init_temperature, add_scalable_noise_to_transforms=False, feed_transforms_into_multiplexer=True) self.final_lr_conv = ConvGnLelu(nf, nf, kernel_size=3, norm=False, activation=True, bias=True) if upscale > 1: n_upscale = int(math.log(upscale, 2)) self.upsample = nn.Sequential( *[UpconvBlock(nf, 64, block=ConvGnLelu, norm=False, activation=True, bias=True) for _ in range(n_upscale)]) else: self.upsample = ConvGnLelu(nf, nf, kernel_size=3, norm=False, activation=True) self.final_hr_conv2 = ConvGnLelu(64, out_nc, kernel_size=3, norm=False, activation=False, bias=False) self.switches = [self.sw1, self.sw2, self.sw3] self.attentions = None self.lr = None self.init_temperature = init_temperature self.final_temperature_step = 10000 def forward(self, x, *args): # The attention_maps debugger outputs . Save that here. self.lr = x.detach().cpu() x = self.model_fea_conv(x) x1, a1 = self.sw1(x, True) x1 = self.intermediate_conv(x1) x2, a2 = self.sw2(x1, True) x2 = self.intermediate_conv2(x2) x3, a3 = self.sw3(x2, True) x_out = self.final_lr_conv(x3) x_out = self.upsample(x_out) x_out = self.final_hr_conv2(x_out) self.attentions = [a1, a2, a3] return x_out 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, 1 + self.init_temperature * (self.final_temperature_step - step) / self.final_temperature_step) self.set_temperature(temp) if step % 200 == 0: output_path = os.path.join(experiments_path, "attention_maps") prefix = "amap_%i_a%i_%%i.png" [save_attention_to_image_rgb(output_path, self.attentions[i], self.transformation_counts, prefix % (step, i), step, output_mag=False) for i in range(len(self.attentions))] torchvision.utils.save_image(self.lr, os.path.join(experiments_path, "attention_maps", "amap_%i_base_image.png" % (step,))) def get_debug_values(self, step, net_name): 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