From fe82785ba57da1f2bd77ef8ab562bb098717a1d0 Mon Sep 17 00:00:00 2001 From: James Betker Date: Sat, 19 Sep 2020 21:47:10 -0600 Subject: [PATCH] Add some new architectures to ssg --- .../archs/StructuredSwitchedGenerator.py | 280 +++++++++++++++++- codes/models/networks.py | 14 +- 2 files changed, 292 insertions(+), 2 deletions(-) diff --git a/codes/models/archs/StructuredSwitchedGenerator.py b/codes/models/archs/StructuredSwitchedGenerator.py index c5fd5923..c68b8619 100644 --- a/codes/models/archs/StructuredSwitchedGenerator.py +++ b/codes/models/archs/StructuredSwitchedGenerator.py @@ -159,7 +159,7 @@ class SSGr1(nn.Module): self.init_temperature = init_temperature self.final_temperature_step = 10000 - def forward(self, x, embedding): + def forward(self, x, *args): noise_stds = [] # The attention_maps debugger outputs . Save that here. self.lr = x.detach().cpu() @@ -223,3 +223,281 @@ class SSGr1(nn.Module): 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 = x + x1, a1 = self.sw1(x1, True, identity=x) + + 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 \ No newline at end of file diff --git a/codes/models/networks.py b/codes/models/networks.py index 8101fb79..672d3c4e 100644 --- a/codes/models/networks.py +++ b/codes/models/networks.py @@ -35,7 +35,8 @@ def define_G(opt, net_key='network_G', scale=None): # Need to adjust the scale the generator sees by the stride since the stride causes a down-sample. gen_scale = scale * initial_stride netG = RRDBNet_arch.RRDBNet(in_nc=opt_net['in_nc'], out_nc=opt_net['out_nc'], - nf=opt_net['nf'], nb=opt_net['nb'], scale=gen_scale, initial_stride=initial_stride) + nf=opt_net['nf'], nb=opt_net['nb'], scale=opt_net['scale'] if 'scale' in opt_net.keys() else gen_scale, + initial_stride=initial_stride) elif which_model == "ConfigurableSwitchedResidualGenerator2": netG = SwitchedGen_arch.ConfigurableSwitchedResidualGenerator2(switch_depth=opt_net['switch_depth'], switch_filters=opt_net['switch_filters'], switch_reductions=opt_net['switch_reductions'], @@ -64,6 +65,14 @@ 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 = 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 == 'ssg_no_embedding': + xforms = opt_net['num_transforms'] if 'num_transforms' in opt_net.keys() else 8 + netG = ssg.SSGNoEmbedding(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 == 'ssg_lite': + xforms = opt_net['num_transforms'] if 'num_transforms' in opt_net.keys() else 8 + netG = ssg.SSGLite(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": @@ -86,6 +95,9 @@ class GradDiscWrapper(torch.nn.Module): def define_D_net(opt_net, img_sz=None, wrap=False): which_model = opt_net['which_model_D'] + if 'image_size' in opt_net.keys(): + img_sz = opt_net['image_size'] + if which_model == 'discriminator_vgg_128': netD = SRGAN_arch.Discriminator_VGG_128(in_nc=opt_net['in_nc'], nf=opt_net['nf'], input_img_factor=img_sz / 128, extra_conv=opt_net['extra_conv']) elif which_model == 'discriminator_vgg_128_gn':