From ccf843800153f92f912b514697b8b6528d121d98 Mon Sep 17 00:00:00 2001 From: James Betker Date: Sun, 13 Sep 2020 20:10:24 -0600 Subject: [PATCH] SPSR5 This is SPSR4, but the multiplexers have access to the output of the transformations for making their decision. --- codes/models/archs/SPSR_arch.py | 133 +++++++++++++++++- .../archs/SwitchedResidualGenerator_arch.py | 66 ++++++++- codes/models/networks.py | 4 + 3 files changed, 198 insertions(+), 5 deletions(-) diff --git a/codes/models/archs/SPSR_arch.py b/codes/models/archs/SPSR_arch.py index 808f3f36..97905b1d 100644 --- a/codes/models/archs/SPSR_arch.py +++ b/codes/models/archs/SPSR_arch.py @@ -5,7 +5,7 @@ import torch.nn.functional as F from models.archs import SPSR_util as B from .RRDBNet_arch import RRDB from models.archs.arch_util import ConvGnLelu, UpconvBlock, ConjoinBlock, ConvGnSilu, MultiConvBlock, ReferenceJoinBlock -from models.archs.SwitchedResidualGenerator_arch import ConvBasisMultiplexer, ConfigurableSwitchComputer, ReferencingConvMultiplexer, ReferenceImageBranch, AdaInConvBlock, ProcessingBranchWithStochasticity, EmbeddingMultiplexer +from models.archs.SwitchedResidualGenerator_arch import ConvBasisMultiplexer, ConfigurableSwitchComputer, ReferencingConvMultiplexer, ReferenceImageBranch, AdaInConvBlock, ProcessingBranchWithStochasticity, EmbeddingMultiplexer, QueryKeyMultiplexer from switched_conv_util import save_attention_to_image_rgb from switched_conv import compute_attention_specificity import functools @@ -540,3 +540,134 @@ class Spsr4(nn.Module): val["switch_%i_specificity" % (i,)] = means[i] val["switch_%i_histogram" % (i,)] = hists[i] return val + + +class Spsr5(nn.Module): + def __init__(self, in_nc, out_nc, nf, xforms=8, upscale=4, init_temperature=10): + super(Spsr5, self).__init__() + n_upscale = int(math.log(upscale, 2)) + + # switch options + transformation_filters = nf + self.transformation_counts = xforms + multiplx_fn = functools.partial(QueryKeyMultiplexer, transformation_filters) + pretransform_fn = functools.partial(ConvGnLelu, 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=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) + self.sw1 = ConfigurableSwitchComputer(transformation_filters, multiplx_fn, + pre_transform_block=pretransform_fn, 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.sw2 = ConfigurableSwitchComputer(transformation_filters, multiplx_fn, + pre_transform_block=pretransform_fn, 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) + self.grad_ref_join = ReferenceJoinBlock(nf, residual_weight_init_factor=.3, final_norm=False) + self.sw_grad = ConfigurableSwitchComputer(transformation_filters, multiplx_fn, + pre_transform_block=pretransform_fn, 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.grad_lr_conv2 = 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) + self.conjoin_ref_join = ReferenceJoinBlock(nf, residual_weight_init_factor=.3) + self.conjoin_sw = ConfigurableSwitchComputer(transformation_filters, multiplx_fn, + pre_transform_block=pretransform_fn, 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_conv1 = ConvGnLelu(nf, nf, kernel_size=3, norm=False, activation=False, bias=True) + self.final_hr_conv2 = ConvGnLelu(nf, out_nc, kernel_size=3, norm=False, activation=False, bias=False) + self.switches = [self.sw1, self.sw2, 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)) + + x2 = x1 + x2, nstd = self.noise_ref_join(x2, torch.randn_like(x2)) + x2, a2 = self.sw2(x2, True, identity=x1, att_in=(x2, embedding)) + noise_stds.append(nstd) + + 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 = self.grad_lr_conv2(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 = x2 + 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=x2, att_in=(x_out, embedding)) + x_out = self.final_lr_conv(x_out) + x_out = self.upsample(x_out) + x_out = self.final_hr_conv1(x_out) + x_out = self.final_hr_conv2(x_out) + noise_stds.append(nstd) + + self.attentions = [a1, a2, 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/SwitchedResidualGenerator_arch.py b/codes/models/archs/SwitchedResidualGenerator_arch.py index a20ed792..aef344b4 100644 --- a/codes/models/archs/SwitchedResidualGenerator_arch.py +++ b/codes/models/archs/SwitchedResidualGenerator_arch.py @@ -175,7 +175,7 @@ class ReferencingConvMultiplexer(nn.Module): class ConfigurableSwitchComputer(nn.Module): def __init__(self, base_filters, multiplexer_net, pre_transform_block, transform_block, transform_count, attention_norm, - init_temp=20, add_scalable_noise_to_transforms=False): + init_temp=20, add_scalable_noise_to_transforms=False, feed_transforms_into_multiplexer=False): super(ConfigurableSwitchComputer, self).__init__() tc = transform_count @@ -187,6 +187,7 @@ class ConfigurableSwitchComputer(nn.Module): self.pre_transform = None self.transforms = nn.ModuleList([transform_block() for _ in range(transform_count)]) self.add_noise = add_scalable_noise_to_transforms + self.feed_transforms_into_multiplexer = feed_transforms_into_multiplexer self.noise_scale = nn.Parameter(torch.full((1,), float(1e-3))) # And the switch itself, including learned scalars @@ -232,6 +233,8 @@ class ConfigurableSwitchComputer(nn.Module): if not isinstance(att_in, tuple): att_in = (att_in,) + if self.feed_transforms_into_multiplexer: + att_in = att_in + (torch.stack(xformed, dim=1),) if memory_checkpointing_enabled: m = checkpoint(self.multiplexer, *att_in) else: @@ -384,6 +387,9 @@ class BackboneEncoder(nn.Module): return combined +# Note to future self: +# Can I do a real transformer here? Such as by having the multiplexer be able to toggle off of transformations by +# their output? The embedding will be used as the "Query" to the "QueryxKey=Value" relationship. # Mutiplexer that combines a structured embedding with a contextual switch input to guide alterations to that input. # @@ -429,13 +435,65 @@ class EmbeddingMultiplexer(nn.Module): x = self.cbl3(x) return x + +class QueryKeyMultiplexer(nn.Module): + def __init__(self, nf, multiplexer_channels, reductions=2): + super(QueryKeyMultiplexer, 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 + 256, kernel_size=1, activation=True, norm=False, bias=True), + ConvGnSilu(reduction_filters + 256, reduction_filters + 128, kernel_size=1, activation=True, norm=True, bias=False), + ConvGnSilu(reduction_filters + 128, 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.cbl1 = ConvGnSilu(nf, nf // 2, kernel_size=1, norm=True, bias=False, num_groups=4) + self.cbl2 = ConvGnSilu(nf // 2, 1, kernel_size=1, 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) + + k = k.view(b, t, f, h, w) # Not sure if this is necessary.. + q = q.view(b, 1, f, h, w).repeat(1, t, 1, 1, 1) + v = q * k + v = v.view(b * t, f, h, w) + + v = self.cbl1(v) + v = self.cbl2(v) + + return v.view(b, t, h, w) + + if __name__ == '__main__': bb = BackboneEncoder(64) - emb = EmbeddingMultiplexer(64, 10) + emb = QueryKeyMultiplexer(64, 10) x = torch.randn(4,3,64,64) - r = torch.randn(4,4,64,64) + r = torch.randn(4,3,128,128) xu = torch.randn(4,64,64,64) cp = torch.zeros((4,2), dtype=torch.long) + trans = [torch.randn(4,64,64,64) for t in range(10)] + b = bb(x, r, cp) - emb(xu, b) \ No newline at end of file + emb(xu, b, trans) \ No newline at end of file diff --git a/codes/models/networks.py b/codes/models/networks.py index 8c8dd887..9c4a855c 100644 --- a/codes/models/networks.py +++ b/codes/models/networks.py @@ -55,6 +55,10 @@ 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.Spsr4(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 == "spsr5": + 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 == "backbone_encoder": netG = SwitchedGen_arch.BackboneEncoder(pretrained_backbone=opt_net['pretrained_spinenet']) else: