From 0918430572ce58ccb4d08516bbe15cc1a1c21965 Mon Sep 17 00:00:00 2001 From: James Betker Date: Tue, 15 Sep 2020 20:59:24 -0600 Subject: [PATCH] SSG network This branches off of SPSR. It is identical but substantially reduced in complexity. It's intended to be my long term working arch. --- .../archs/StructuredSwitchedGenerator.py | 219 ++++++++++++++++++ codes/models/archs/arch_util.py | 6 +- codes/models/networks.py | 5 + 3 files changed, 227 insertions(+), 3 deletions(-) create mode 100644 codes/models/archs/StructuredSwitchedGenerator.py diff --git a/codes/models/archs/StructuredSwitchedGenerator.py b/codes/models/archs/StructuredSwitchedGenerator.py new file mode 100644 index 00000000..89f76e6b --- /dev/null +++ b/codes/models/archs/StructuredSwitchedGenerator.py @@ -0,0 +1,219 @@ +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 + + +# 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, nf // 2, block=ConvGnLelu, norm=False, activation=True, bias=True) for _ in range(n_upscale)]) + self.final_hr_conv2 = ConvGnLelu(nf // 2, 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.init_temperature = init_temperature + self.final_temperature_step = 10000 + + def forward(self, x, embedding): + noise_stds = [] + + 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", "a%i") + prefix = "attention_map_%i_%%i.png" % (step,) + [save_attention_to_image_rgb(output_path % (i,), self.attentions[i], self.transformation_counts, prefix, step) for i in range(len(self.attentions))] + + 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, + "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 diff --git a/codes/models/archs/arch_util.py b/codes/models/archs/arch_util.py index 22bde274..94f430ac 100644 --- a/codes/models/archs/arch_util.py +++ b/codes/models/archs/arch_util.py @@ -456,12 +456,12 @@ class ConjoinBlock(nn.Module): # Designed explicitly to join a mainline trunk with reference data. Implemented as a residual branch. class ReferenceJoinBlock(nn.Module): - def __init__(self, nf, residual_weight_init_factor=1, block=ConvGnLelu, final_norm=False): + def __init__(self, nf, residual_weight_init_factor=1, block=ConvGnLelu, final_norm=False, kernel_size=3, depth=3): super(ReferenceJoinBlock, self).__init__() - self.branch = MultiConvBlock(nf * 2, nf + nf // 2, nf, kernel_size=3, depth=3, + self.branch = MultiConvBlock(nf * 2, nf + nf // 2, nf, kernel_size=kernel_size, depth=depth, scale_init=residual_weight_init_factor, norm=False, weight_init_factor=residual_weight_init_factor) - self.join_conv = block(nf, nf, norm=final_norm, bias=False, activation=True) + self.join_conv = block(nf, nf, kernel_size=kernel_size, norm=final_norm, bias=False, activation=True) def forward(self, x, ref): joined = torch.cat([x, ref], dim=1) diff --git a/codes/models/networks.py b/codes/models/networks.py index e5d66744..8101fb79 100644 --- a/codes/models/networks.py +++ b/codes/models/networks.py @@ -9,6 +9,7 @@ import models.archs.RRDBNet_arch as RRDBNet_arch import models.archs.feature_arch as feature_arch import models.archs.SwitchedResidualGenerator_arch as SwitchedGen_arch import models.archs.SPSR_arch as spsr +import models.archs.StructuredSwitchedGenerator as ssg from collections import OrderedDict logger = logging.getLogger('base') @@ -59,6 +60,10 @@ def define_G(opt, net_key='network_G', scale=None): xforms = opt_net['num_transforms'] if 'num_transforms' in opt_net.keys() else 8 netG = spsr.Spsr5(in_nc=3, out_nc=3, nf=opt_net['nf'], xforms=xforms, upscale=opt_net['scale'], init_temperature=opt_net['temperature'] if 'temperature' in opt_net.keys() else 10) + elif which_model == "ssgr1": + xforms = opt_net['num_transforms'] if 'num_transforms' in opt_net.keys() else 8 + netG = ssg.SSGr1(in_nc=3, out_nc=3, nf=opt_net['nf'], xforms=xforms, upscale=opt_net['scale'], + init_temperature=opt_net['temperature'] if 'temperature' in opt_net.keys() else 10) elif which_model == "backbone_encoder": netG = SwitchedGen_arch.BackboneEncoder(pretrained_backbone=opt_net['pretrained_spinenet']) elif which_model == "backbone_encoder_no_ref":