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.
This commit is contained in:
James Betker
parent
c833bd1eac
commit
0918430572
219
codes/models/archs/StructuredSwitchedGenerator.py
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codes/models/archs/StructuredSwitchedGenerator.py
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import math
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import functools
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from models.archs.arch_util import MultiConvBlock, ConvGnLelu, ConvGnSilu, ReferenceJoinBlock
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from models.archs.SwitchedResidualGenerator_arch import ConfigurableSwitchComputer
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from models.archs.SPSR_arch import ImageGradientNoPadding
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from torch import nn
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import torch
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import torch.nn.functional as F
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from switched_conv_util import save_attention_to_image_rgb
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from switched_conv import compute_attention_specificity
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import os
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# VGG-style layer with Conv(stride2)->BN->Activation->Conv->BN->Activation
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# Doubles the input filter count.
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class HalvingProcessingBlock(nn.Module):
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def __init__(self, filters):
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super(HalvingProcessingBlock, self).__init__()
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self.bnconv1 = ConvGnSilu(filters, filters * 2, kernel_size=1, stride=2, norm=False, bias=False)
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self.bnconv2 = ConvGnSilu(filters * 2, filters * 2, norm=True, bias=False)
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def forward(self, x):
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x = self.bnconv1(x)
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return self.bnconv2(x)
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class ExpansionBlock2(nn.Module):
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def __init__(self, filters_in, filters_out=None, block=ConvGnSilu):
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super(ExpansionBlock2, self).__init__()
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if filters_out is None:
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filters_out = filters_in // 2
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self.decimate = block(filters_in, filters_out, kernel_size=1, bias=False, activation=True, norm=False)
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self.process_passthrough = block(filters_out, filters_out, kernel_size=3, bias=True, activation=True, norm=False)
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self.conjoin = block(filters_out*2, filters_out*2, kernel_size=1, bias=False, activation=True, norm=False)
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self.reduce = block(filters_out*2, filters_out, kernel_size=1, bias=False, activation=False, norm=True)
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# input is the feature signal with shape (b, f, w, h)
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# passthrough is the structure signal with shape (b, f/2, w*2, h*2)
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# output is conjoined upsample with shape (b, f/2, w*2, h*2)
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def forward(self, input, passthrough):
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x = F.interpolate(input, scale_factor=2, mode="nearest")
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x = self.decimate(x)
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p = self.process_passthrough(passthrough)
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x = self.conjoin(torch.cat([x, p], dim=1))
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return self.reduce(x)
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# Basic convolutional upsampling block that uses interpolate.
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class UpconvBlock(nn.Module):
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def __init__(self, filters_in, filters_out=None, block=ConvGnSilu, norm=True, activation=True, bias=False):
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super(UpconvBlock, self).__init__()
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self.reduce = block(filters_in, filters_out, kernel_size=1, bias=False, activation=False, norm=False)
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self.process = block(filters_out, filters_out, kernel_size=3, bias=bias, activation=activation, norm=norm)
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def forward(self, x):
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x = self.reduce(x)
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x = F.interpolate(x, scale_factor=2, mode="nearest")
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return self.process(x)
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class SSGMultiplexer(nn.Module):
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def __init__(self, nf, multiplexer_channels, reductions=2):
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super(SSGMultiplexer, self).__init__()
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# Blocks used to create the query
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self.input_process = ConvGnSilu(nf, nf, activation=True, norm=False, bias=True)
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self.embedding_process = ConvGnSilu(256, 256, activation=True, norm=False, bias=True)
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self.reduction_blocks = nn.ModuleList([HalvingProcessingBlock(nf * 2 ** i) for i in range(reductions)])
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reduction_filters = nf * 2 ** reductions
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self.processing_blocks = nn.Sequential(
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ConvGnSilu(reduction_filters + 256, reduction_filters + 128, kernel_size=1, activation=True, norm=False, bias=True),
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ConvGnSilu(reduction_filters + 128, reduction_filters, kernel_size=3, activation=True, norm=True, bias=False))
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self.expansion_blocks = nn.ModuleList([ExpansionBlock2(reduction_filters // (2 ** i)) for i in range(reductions)])
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# Blocks used to create the key
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self.key_process = ConvGnSilu(nf, nf, kernel_size=1, activation=True, norm=False, bias=True)
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# Postprocessing blocks.
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self.query_key_combine = ConvGnSilu(nf*2, nf, kernel_size=1, activation=True, norm=False, bias=False)
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self.cbl1 = ConvGnSilu(nf, nf // 4, kernel_size=1, activation=True, norm=True, bias=False, num_groups=4)
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self.cbl2 = ConvGnSilu(nf // 4, 1, kernel_size=1, activation=False, norm=False, bias=False)
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def forward(self, x, embedding, transformations):
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q = self.input_process(x)
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embedding = self.embedding_process(embedding)
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reduction_identities = []
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for b in self.reduction_blocks:
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reduction_identities.append(q)
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q = b(q)
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q = self.processing_blocks(torch.cat([q, embedding], dim=1))
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for i, b in enumerate(self.expansion_blocks):
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q = b(q, reduction_identities[-i - 1])
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b, t, f, h, w = transformations.shape
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k = transformations.view(b * t, f, h, w)
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k = self.key_process(k)
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q = q.view(b, 1, f, h, w).repeat(1, t, 1, 1, 1).view(b * t, f, h, w)
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v = self.query_key_combine(torch.cat([q, k], dim=1))
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v = self.cbl1(v)
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v = self.cbl2(v)
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return v.view(b, t, h, w)
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class SSGr1(nn.Module):
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def __init__(self, in_nc, out_nc, nf, xforms=8, upscale=4, init_temperature=10):
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super(SSGr1, self).__init__()
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n_upscale = int(math.log(upscale, 2))
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# switch options
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transformation_filters = nf
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self.transformation_counts = xforms
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multiplx_fn = functools.partial(SSGMultiplexer, transformation_filters)
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transform_fn = functools.partial(MultiConvBlock, transformation_filters, int(transformation_filters * 1.25),
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transformation_filters, kernel_size=3, depth=3,
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weight_init_factor=.1)
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# Feature branch
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self.model_fea_conv = ConvGnLelu(in_nc, nf, kernel_size=3, norm=False, activation=False)
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self.noise_ref_join = ReferenceJoinBlock(nf, residual_weight_init_factor=.1, kernel_size=1, depth=2)
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self.sw1 = ConfigurableSwitchComputer(transformation_filters, multiplx_fn,
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pre_transform_block=None, transform_block=transform_fn,
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attention_norm=True,
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transform_count=self.transformation_counts, init_temp=init_temperature,
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add_scalable_noise_to_transforms=False, feed_transforms_into_multiplexer=True)
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self.feature_lr_conv = ConvGnLelu(nf, nf, kernel_size=3, norm=True, activation=False)
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self.feature_lr_conv2 = ConvGnLelu(nf, nf, kernel_size=3, norm=False, activation=False, bias=False)
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# Grad branch. Note - groupnorm on this branch is REALLY bad. Avoid it like the plague.
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self.get_g_nopadding = ImageGradientNoPadding()
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self.grad_conv = ConvGnLelu(in_nc, nf, kernel_size=3, norm=False, activation=False, bias=False)
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self.noise_ref_join_grad = ReferenceJoinBlock(nf, residual_weight_init_factor=.1, kernel_size=1, depth=2)
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self.grad_ref_join = ReferenceJoinBlock(nf, residual_weight_init_factor=.3, final_norm=False, kernel_size=1, depth=2)
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self.sw_grad = ConfigurableSwitchComputer(transformation_filters, multiplx_fn,
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pre_transform_block=None, transform_block=transform_fn,
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attention_norm=True,
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transform_count=self.transformation_counts // 2, init_temp=init_temperature,
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add_scalable_noise_to_transforms=False, feed_transforms_into_multiplexer=True)
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self.grad_lr_conv = ConvGnLelu(nf, nf, kernel_size=3, norm=False, activation=True, bias=True)
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self.upsample_grad = nn.Sequential(*[UpconvBlock(nf, nf // 2, block=ConvGnLelu, norm=False, activation=True, bias=False) for _ in range(n_upscale)])
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self.grad_branch_output_conv = ConvGnLelu(nf // 2, out_nc, kernel_size=1, norm=False, activation=False, bias=True)
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# Join branch (grad+fea)
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self.noise_ref_join_conjoin = ReferenceJoinBlock(nf, residual_weight_init_factor=.1, kernel_size=1, depth=2)
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self.conjoin_ref_join = ReferenceJoinBlock(nf, residual_weight_init_factor=.3, kernel_size=1, depth=2)
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self.conjoin_sw = ConfigurableSwitchComputer(transformation_filters, multiplx_fn,
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pre_transform_block=None, transform_block=transform_fn,
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attention_norm=True,
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transform_count=self.transformation_counts, init_temp=init_temperature,
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add_scalable_noise_to_transforms=False, feed_transforms_into_multiplexer=True)
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self.final_lr_conv = ConvGnLelu(nf, nf, kernel_size=3, norm=False, activation=True, bias=True)
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self.upsample = nn.Sequential(*[UpconvBlock(nf, nf // 2, block=ConvGnLelu, norm=False, activation=True, bias=True) for _ in range(n_upscale)])
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self.final_hr_conv2 = ConvGnLelu(nf // 2, out_nc, kernel_size=3, norm=False, activation=False, bias=False)
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self.switches = [self.sw1, self.sw_grad, self.conjoin_sw]
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self.attentions = None
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self.init_temperature = init_temperature
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self.final_temperature_step = 10000
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def forward(self, x, embedding):
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noise_stds = []
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x_grad = self.get_g_nopadding(x)
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x = self.model_fea_conv(x)
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x1 = x
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x1, a1 = self.sw1(x1, True, identity=x, att_in=(x1, embedding))
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x_grad = self.grad_conv(x_grad)
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x_grad_identity = x_grad
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x_grad, nstd = self.noise_ref_join_grad(x_grad, torch.randn_like(x_grad))
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x_grad, grad_fea_std = self.grad_ref_join(x_grad, x1)
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x_grad, a3 = self.sw_grad(x_grad, True, identity=x_grad_identity, att_in=(x_grad, embedding))
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x_grad = self.grad_lr_conv(x_grad)
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x_grad_out = self.upsample_grad(x_grad)
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x_grad_out = self.grad_branch_output_conv(x_grad_out)
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noise_stds.append(nstd)
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x_out = x1
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x_out, nstd = self.noise_ref_join_conjoin(x_out, torch.randn_like(x_out))
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x_out, fea_grad_std = self.conjoin_ref_join(x_out, x_grad)
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x_out, a4 = self.conjoin_sw(x_out, True, identity=x1, att_in=(x_out, embedding))
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x_out = self.final_lr_conv(x_out)
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x_out = self.upsample(x_out)
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x_out = self.final_hr_conv2(x_out)
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noise_stds.append(nstd)
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self.attentions = [a1, a3, a4]
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self.noise_stds = torch.stack(noise_stds).mean().detach().cpu()
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self.grad_fea_std = grad_fea_std.detach().cpu()
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self.fea_grad_std = fea_grad_std.detach().cpu()
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return x_grad_out, x_out, x_grad
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def set_temperature(self, temp):
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[sw.set_temperature(temp) for sw in self.switches]
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def update_for_step(self, step, experiments_path='.'):
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if self.attentions:
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temp = max(1, 1 + self.init_temperature *
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(self.final_temperature_step - step) / self.final_temperature_step)
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self.set_temperature(temp)
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if step % 200 == 0:
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output_path = os.path.join(experiments_path, "attention_maps", "a%i")
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prefix = "attention_map_%i_%%i.png" % (step,)
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[save_attention_to_image_rgb(output_path % (i,), self.attentions[i], self.transformation_counts, prefix, step) for i in range(len(self.attentions))]
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def get_debug_values(self, step):
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temp = self.switches[0].switch.temperature
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mean_hists = [compute_attention_specificity(att, 2) for att in self.attentions]
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means = [i[0] for i in mean_hists]
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hists = [i[1].clone().detach().cpu().flatten() for i in mean_hists]
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val = {"switch_temperature": temp,
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"noise_branch_std_dev": self.noise_stds,
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"grad_branch_feat_intg_std_dev": self.grad_fea_std,
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"conjoin_branch_grad_intg_std_dev": self.fea_grad_std}
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for i in range(len(means)):
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val["switch_%i_specificity" % (i,)] = means[i]
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val["switch_%i_histogram" % (i,)] = hists[i]
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return val
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@ -456,12 +456,12 @@ class ConjoinBlock(nn.Module):
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# Designed explicitly to join a mainline trunk with reference data. Implemented as a residual branch.
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class ReferenceJoinBlock(nn.Module):
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def __init__(self, nf, residual_weight_init_factor=1, block=ConvGnLelu, final_norm=False):
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def __init__(self, nf, residual_weight_init_factor=1, block=ConvGnLelu, final_norm=False, kernel_size=3, depth=3):
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super(ReferenceJoinBlock, self).__init__()
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self.branch = MultiConvBlock(nf * 2, nf + nf // 2, nf, kernel_size=3, depth=3,
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self.branch = MultiConvBlock(nf * 2, nf + nf // 2, nf, kernel_size=kernel_size, depth=depth,
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scale_init=residual_weight_init_factor, norm=False,
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weight_init_factor=residual_weight_init_factor)
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self.join_conv = block(nf, nf, norm=final_norm, bias=False, activation=True)
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self.join_conv = block(nf, nf, kernel_size=kernel_size, norm=final_norm, bias=False, activation=True)
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def forward(self, x, ref):
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joined = torch.cat([x, ref], dim=1)
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@ -9,6 +9,7 @@ import models.archs.RRDBNet_arch as RRDBNet_arch
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import models.archs.feature_arch as feature_arch
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import models.archs.SwitchedResidualGenerator_arch as SwitchedGen_arch
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import models.archs.SPSR_arch as spsr
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import models.archs.StructuredSwitchedGenerator as ssg
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from collections import OrderedDict
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logger = logging.getLogger('base')
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@ -59,6 +60,10 @@ def define_G(opt, net_key='network_G', scale=None):
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xforms = opt_net['num_transforms'] if 'num_transforms' in opt_net.keys() else 8
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netG = spsr.Spsr5(in_nc=3, out_nc=3, nf=opt_net['nf'], xforms=xforms, upscale=opt_net['scale'],
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init_temperature=opt_net['temperature'] if 'temperature' in opt_net.keys() else 10)
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elif which_model == "ssgr1":
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xforms = opt_net['num_transforms'] if 'num_transforms' in opt_net.keys() else 8
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netG = ssg.SSGr1(in_nc=3, out_nc=3, nf=opt_net['nf'], xforms=xforms, upscale=opt_net['scale'],
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init_temperature=opt_net['temperature'] if 'temperature' in opt_net.keys() else 10)
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elif which_model == "backbone_encoder":
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netG = SwitchedGen_arch.BackboneEncoder(pretrained_backbone=opt_net['pretrained_spinenet'])
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elif which_model == "backbone_encoder_no_ref":
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