576 lines
31 KiB
Python
576 lines
31 KiB
Python
import functools
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import math
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import torch
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import torch.nn.functional as F
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from torch import nn
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from models.archs.SPSR_arch import ImageGradientNoPadding
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from models.archs.SwitchedResidualGenerator_arch import ConfigurableSwitchComputer, gather_2d, SwitchModelBase
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from models.archs.arch_util import MultiConvBlock, ConvGnLelu, ConvGnSilu, ReferenceJoinBlock
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from utils.util import checkpoint
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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, factor=2):
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super(HalvingProcessingBlock, self).__init__()
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self.bnconv1 = ConvGnSilu(filters, filters, norm=False, bias=False)
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self.bnconv2 = ConvGnSilu(filters, int(filters * factor), kernel_size=1, stride=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, factor=2):
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super(ExpansionBlock2, self).__init__()
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if filters_out is None:
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filters_out = int(filters_in / factor)
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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 QueryKeyMultiplexer(nn.Module):
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def __init__(self, nf, multiplexer_channels, embedding_channels=216, reductions=3):
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super(QueryKeyMultiplexer, 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(embedding_channels, 128, kernel_size=1, activation=True, norm=False, bias=True)
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self.reduction_blocks = nn.ModuleList([HalvingProcessingBlock(int(nf * 1.5 ** i), factor=1.5) for i in range(reductions)])
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reduction_filters = int(nf * 1.5 ** reductions)
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self.processing_blocks = nn.Sequential(
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ConvGnSilu(reduction_filters + 128, reduction_filters + 64, kernel_size=1, activation=True, norm=False, bias=True),
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ConvGnSilu(reduction_filters + 64, reduction_filters, kernel_size=1, activation=True, norm=False, bias=False),
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ConvGnSilu(reduction_filters, reduction_filters, kernel_size=3, activation=True, norm=True, bias=False),
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ConvGnSilu(reduction_filters, reduction_filters, kernel_size=3, activation=True, norm=True, bias=False))
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self.expansion_blocks = nn.ModuleList([ExpansionBlock2(int(reduction_filters // (1.5 ** i)), factor=1.5) 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=False)
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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 // 2, kernel_size=1, norm=True, bias=False, num_groups=4)
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self.cbl2 = ConvGnSilu(nf // 2, 1, kernel_size=1, 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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# Computes a linear latent by performing processing on the reference image and returning the filters of a single point,
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# which should be centered on the image patch being processed.
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#
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# Output is base_filters * 1.5^3.
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class ReferenceImageBranch(nn.Module):
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def __init__(self, base_filters=64):
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super(ReferenceImageBranch, self).__init__()
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final_filters = int(base_filters*1.5**3)
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self.features = nn.Sequential(ConvGnSilu(4, base_filters, kernel_size=7, bias=True),
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HalvingProcessingBlock(base_filters, factor=1.5),
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HalvingProcessingBlock(int(base_filters*1.5), factor=1.5),
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HalvingProcessingBlock(int(base_filters*1.5**2), factor=1.5),
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ConvGnSilu(final_filters, final_filters, activation=True, norm=True, bias=False))
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# center_point is a [b,2] long tensor describing the center point of where the patch was taken from the reference
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# image.
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def forward(self, x, center_point):
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x = self.features(x)
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return gather_2d(x, center_point // 8) # Divide by 8 to scale the center_point down.
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class SwitchWithReference(nn.Module):
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def __init__(self, nf, num_transforms, init_temperature=10, has_ref=True):
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super(SwitchWithReference, self).__init__()
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self.nf = nf
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self.transformation_counts = num_transforms
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multiplx_fn = functools.partial(QueryKeyMultiplexer, nf)
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transform_fn = functools.partial(MultiConvBlock, nf, int(nf * 1.25), nf, kernel_size=3, depth=4, weight_init_factor=.1)
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if has_ref:
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self.ref_join = ReferenceJoinBlock(nf, residual_weight_init_factor=.3, final_norm=False, kernel_size=1, depth=2)
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else:
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self.ref_join = None
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self.switch = ConfigurableSwitchComputer(nf, 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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def forward(self, x, mplex_ref=None, ref=None):
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if self.ref_join is not None:
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branch, ref_std = self.ref_join(x, ref)
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return self.switch(branch, identity=x, att_in=(branch, mplex_ref)) + (ref_std,)
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else:
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return self.switch(x, identity=x, att_in=(x, mplex_ref))
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class SSGr1(SwitchModelBase):
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def __init__(self, in_nc, out_nc, nf, xforms=8, upscale=4, init_temperature=10, recurrent=False):
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super(SSGr1, self).__init__(init_temperature, 10000)
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n_upscale = int(math.log(upscale, 2))
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self.nf = nf
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if recurrent:
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self.recurrent = True
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self.recurrent_process = ConvGnLelu(in_nc, nf, kernel_size=3, stride=2, norm=False, bias=True, activation=False)
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self.recurrent_join = ReferenceJoinBlock(nf, residual_weight_init_factor=.01, final_norm=False, kernel_size=1, depth=3, join=False)
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else:
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self.recurrent = False
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# processing the input embedding
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self.reference_embedding = ReferenceImageBranch(nf)
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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.sw1 = SwitchWithReference(nf, xforms, init_temperature, has_ref=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.sw_grad = SwitchWithReference(nf, xforms // 2, init_temperature, has_ref=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 = UpconvBlock(nf, nf // 2, block=ConvGnLelu, norm=False, activation=True, bias=False)
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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.conjoin_sw = SwitchWithReference(nf, xforms, init_temperature, has_ref=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 = UpconvBlock(nf, nf // 2, block=ConvGnLelu, norm=False, activation=True, bias=True)
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self.final_hr_conv1 = ConvGnLelu(nf // 2, nf // 2, kernel_size=3, norm=False, activation=False, bias=True)
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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.switch, self.sw_grad.switch, self.conjoin_sw.switch]
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def forward(self, x, ref, ref_center, save_attentions=True, recurrent=None):
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# The attention_maps debugger outputs <x>. Save that here.
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self.lr = x.detach().cpu()
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# If we're not saving attention, we also shouldn't be updating the attention norm. This is because the attention
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# norm should only be getting updates with new data, not recurrent generator sampling.
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for sw in self.switches:
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sw.set_update_attention_norm(save_attentions)
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x_grad = self.get_g_nopadding(x)
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ref_code = checkpoint(self.reference_embedding, ref, ref_center)
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ref_embedding = ref_code.view(-1, ref_code.shape[1], 1, 1).repeat(1, 1, x.shape[2] // 8, x.shape[3] // 8)
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x = self.model_fea_conv(x)
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if self.recurrent:
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rec = self.recurrent_process(recurrent)
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x, recurrent_join_std = self.recurrent_join(x, rec)
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else:
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recurrent_join_std = 0
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x1, a1 = checkpoint(self.sw1, x, ref_embedding)
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x_grad = self.grad_conv(x_grad)
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x_grad, a3, grad_fea_std = checkpoint(self.sw_grad, x_grad, ref_embedding, x1)
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x_grad = checkpoint(self.grad_lr_conv, x_grad)
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x_grad_out = checkpoint(self.upsample_grad, x_grad)
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x_grad_out = checkpoint(self.grad_branch_output_conv, x_grad_out)
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x_out, a4, fea_grad_std = checkpoint(self.conjoin_sw, x1, ref_embedding, x_grad)
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x_out = checkpoint(self.final_lr_conv, x_out)
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x_out = checkpoint(self.upsample, x_out)
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x_out = checkpoint(self.final_hr_conv2, x_out)
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if save_attentions:
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self.attentions = [a1, a3, a4]
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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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class StackedSwitchGenerator(SwitchModelBase):
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def __init__(self, in_nc, out_nc, nf, xforms=8, upscale=4, init_temperature=10):
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super(StackedSwitchGenerator, self).__init__(init_temperature, 10000)
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n_upscale = int(math.log(upscale, 2))
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self.nf = nf
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# processing the input embedding
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self.reference_embedding = ReferenceImageBranch(nf)
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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.sw1 = SwitchWithReference(nf, xforms, init_temperature, has_ref=False)
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self.sw2 = SwitchWithReference(nf, xforms, init_temperature, has_ref=False)
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self.sw3 = SwitchWithReference(nf, xforms, init_temperature, has_ref=False)
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self.switches = [self.sw1.switch, self.sw2.switch, self.sw3.switch]
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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 = UpconvBlock(nf, nf // 2, block=ConvGnLelu, norm=False, activation=True, bias=True)
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self.final_hr_conv1 = ConvGnLelu(nf // 2, nf // 2, kernel_size=3, norm=False, activation=False, bias=True)
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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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def forward(self, x, ref, ref_center, save_attentions=True):
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# The attention_maps debugger outputs <x>. Save that here.
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self.lr = x.detach().cpu()
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ref_code = checkpoint(self.reference_embedding, ref, ref_center)
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ref_embedding = ref_code.view(-1, ref_code.shape[1], 1, 1).repeat(1, 1, x.shape[2] // 8, x.shape[3] // 8)
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x = self.model_fea_conv(x)
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x1, a1 = checkpoint(self.sw1, x, ref_embedding)
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x2, a2 = checkpoint(self.sw2, x1, ref_embedding)
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x3, a3 = checkpoint(self.sw3, x2, ref_embedding)
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x_out = checkpoint(self.final_lr_conv, x3)
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x_out = checkpoint(self.upsample, x_out)
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x_out = checkpoint(self.final_hr_conv2, x_out)
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if save_attentions:
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self.attentions = [a1, a3, a3]
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return x_out,
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class SSGDeep(SwitchModelBase):
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def __init__(self, in_nc, out_nc, nf, xforms=8, upscale=4, init_temperature=10, recurrent=False):
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super(SSGDeep, self).__init__(init_temperature, 10000)
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n_upscale = int(math.log(upscale, 2))
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self.nf = nf
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# processing the input embedding
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if recurrent:
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self.recurrent = True
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self.recurrent_process = ConvGnLelu(in_nc, nf, kernel_size=3, stride=2, norm=False, bias=True, activation=False)
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self.recurrent_join = ReferenceJoinBlock(nf, residual_weight_init_factor=.01, final_norm=False, kernel_size=1, depth=3, join=False)
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else:
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self.recurrent = False
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self.reference_embedding = ReferenceImageBranch(nf)
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# Feature branch
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self.model_fea_conv = ConvGnLelu(in_nc, nf, kernel_size=7, norm=False, activation=False)
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self.sw1 = SwitchWithReference(nf, xforms, init_temperature, has_ref=False)
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self.sw2 = SwitchWithReference(nf, xforms, init_temperature, has_ref=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=7, norm=False, activation=False, bias=False)
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self.sw_grad = SwitchWithReference(nf, xforms // 2, init_temperature, has_ref=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 = UpconvBlock(nf, nf // 2, block=ConvGnLelu, norm=False, activation=True, bias=False)
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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.conjoin_sw = SwitchWithReference(nf, xforms, init_temperature, has_ref=True)
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self.sw4 = SwitchWithReference(nf, xforms, init_temperature, has_ref=False)
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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 = UpconvBlock(nf, nf // 2, block=ConvGnLelu, norm=False, activation=True, bias=True)
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self.final_hr_conv1 = ConvGnLelu(nf // 2, nf // 2, kernel_size=3, norm=False, activation=False, bias=True)
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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.switch, self.sw2.switch, self.sw_grad.switch, self.conjoin_sw.switch, self.sw4.switch]
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def forward(self, x, ref, ref_center, save_attentions=True, recurrent=None):
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# The attention_maps debugger outputs <x>. Save that here.
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self.lr = x.detach().cpu()
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# If we're not saving attention, we also shouldn't be updating the attention norm. This is because the attention
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# norm should only be getting updates with new data, not recurrent generator sampling.
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for sw in self.switches:
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sw.set_update_attention_norm(save_attentions)
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x_grad = self.get_g_nopadding(x)
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ref_code = checkpoint(self.reference_embedding, ref, ref_center)
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ref_embedding = ref_code.view(-1, ref_code.shape[1], 1, 1).repeat(1, 1, x.shape[2] // 8, x.shape[3] // 8)
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x = self.model_fea_conv(x)
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if self.recurrent:
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rec = self.recurrent_process(recurrent)
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x, recurrent_std = self.recurrent_join(x, rec)
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x1, a1 = checkpoint(self.sw1, x, ref_embedding)
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x2, a2 = checkpoint(self.sw2, x1, ref_embedding)
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x_grad = self.grad_conv(x_grad)
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x_grad, a3, grad_fea_std = checkpoint(self.sw_grad, x_grad, ref_embedding, x1)
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x_grad = checkpoint(self.grad_lr_conv, x_grad)
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x_grad_out = checkpoint(self.upsample_grad, x_grad)
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x_grad_out = checkpoint(self.grad_branch_output_conv, x_grad_out)
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x3, a4, fea_grad_std = checkpoint(self.conjoin_sw, x2, ref_embedding, x_grad)
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x_out, a5 = checkpoint(self.sw4, x3, ref_embedding)
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x_out = checkpoint(self.final_lr_conv, x_out)
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x_out = checkpoint(self.upsample, x_out)
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x_out = checkpoint(self.final_hr_conv2, x_out)
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if save_attentions:
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self.attentions = [a1, a2, a3, a4, a5]
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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
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class StackedSwitchGenerator5Layer(SwitchModelBase):
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def __init__(self, in_nc, out_nc, nf, xforms=8, upscale=4, init_temperature=10):
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super(StackedSwitchGenerator5Layer, self).__init__(init_temperature, 10000)
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n_upscale = int(math.log(upscale, 2))
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self.nf = nf
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# processing the input embedding
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self.reference_embedding = ReferenceImageBranch(nf)
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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.sw1 = SwitchWithReference(nf, xforms, init_temperature, has_ref=False)
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self.sw2 = SwitchWithReference(nf, xforms // 2, init_temperature, has_ref=False)
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self.sw3 = SwitchWithReference(nf, xforms // 2, init_temperature, has_ref=False)
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self.sw4 = SwitchWithReference(nf, xforms // 2, init_temperature, has_ref=False)
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self.sw5 = SwitchWithReference(nf, xforms, init_temperature, has_ref=False)
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self.switches = [self.sw1.switch, self.sw2.switch, self.sw3.switch, self.sw4.switch, self.sw5.switch]
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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 = UpconvBlock(nf, nf // 2, block=ConvGnLelu, norm=False, activation=True, bias=True)
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self.final_hr_conv1 = ConvGnLelu(nf // 2, nf // 2, kernel_size=3, norm=False, activation=False, bias=True)
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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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def forward(self, x, ref, ref_center, save_attentions=True):
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# The attention_maps debugger outputs <x>. Save that here.
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self.lr = x.detach().cpu()
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# If we're not saving attention, we also shouldn't be updating the attention norm. This is because the attention
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# norm should only be getting updates with new data, not recurrent generator sampling.
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for sw in self.switches:
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sw.set_update_attention_norm(save_attentions)
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ref_code = checkpoint(self.reference_embedding, ref, ref_center)
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ref_embedding = ref_code.view(-1, ref_code.shape[1], 1, 1).repeat(1, 1, x.shape[2] // 8, x.shape[3] // 8)
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x = self.model_fea_conv(x)
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x1, a1 = checkpoint(self.sw1, x, ref_embedding)
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x2, a2 = checkpoint(self.sw2, x1, ref_embedding)
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x3, a3 = checkpoint(self.sw3, x2, ref_embedding)
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x4, a4 = checkpoint(self.sw4, x3, ref_embedding)
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x5, a5 = checkpoint(self.sw5, x4, ref_embedding)
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x_out = checkpoint(self.final_lr_conv, x5)
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x_out = checkpoint(self.upsample, x_out)
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x_out = checkpoint(self.final_hr_conv2, x_out)
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if save_attentions:
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self.attentions = [a1, a3, a3, a4, a5]
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return x_out,
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class StackedSwitchGenerator2xTeco(SwitchModelBase):
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def __init__(self, nf, xforms=8, init_temperature=10):
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super(StackedSwitchGenerator2xTeco, self).__init__(init_temperature, 10000)
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self.nf = nf
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# processing the input embedding
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self.reference_embedding = ReferenceImageBranch(nf)
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# Feature branch
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self.model_fea_conv = ConvGnLelu(3, nf, kernel_size=7, norm=False, activation=False, bias=True)
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self.model_recurrent_conv = ConvGnLelu(3, nf, kernel_size=3, stride=2, norm=False, activation=False, bias=True)
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self.model_fea_recurrent_combine = ConvGnLelu(nf*2, nf, 1, activation=False, norm=False, bias=False)
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self.sw1 = SwitchWithReference(nf, xforms, init_temperature, has_ref=False)
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self.sw2 = SwitchWithReference(nf, xforms // 2, init_temperature, has_ref=False)
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self.sw3 = SwitchWithReference(nf, xforms // 2, init_temperature, has_ref=False)
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self.sw4 = SwitchWithReference(nf, xforms // 2, init_temperature, has_ref=False)
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self.sw5 = SwitchWithReference(nf, xforms, init_temperature, has_ref=False)
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self.switches = [self.sw1.switch, self.sw2.switch, self.sw3.switch, self.sw4.switch, self.sw5.switch]
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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 = UpconvBlock(nf, nf // 2, block=ConvGnLelu, norm=False, activation=True, bias=True)
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self.final_hr_conv1 = ConvGnLelu(nf // 2, nf // 2, kernel_size=3, norm=False, activation=False, bias=True)
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self.final_hr_conv2 = ConvGnLelu(nf // 2, 3, kernel_size=3, norm=False, activation=False, bias=False)
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def forward(self, x, recurrent, ref, ref_center, save_attentions=True):
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# The attention_maps debugger outputs <x>. Save that here.
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self.lr = x.detach().cpu()
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# If we're not saving attention, we also shouldn't be updating the attention norm. This is because the attention
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# norm should only be getting updates with new data, not recurrent generator sampling.
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for sw in self.switches:
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sw.set_update_attention_norm(save_attentions)
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ref_code = checkpoint(self.reference_embedding, ref, ref_center)
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ref_embedding = ref_code.view(-1, ref_code.shape[1], 1, 1).repeat(1, 1, x.shape[2] // 8, x.shape[3] // 8)
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x = self.model_fea_conv(x)
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rec = self.model_recurrent_conv(recurrent)
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x = self.model_fea_recurrent_combine(torch.cat([x, rec], dim=1))
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x1, a1 = checkpoint(self.sw1, x, ref_embedding)
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x2, a2 = checkpoint(self.sw2, x1, ref_embedding)
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x3, a3 = checkpoint(self.sw3, x2, ref_embedding)
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x4, a4 = checkpoint(self.sw4, x3, ref_embedding)
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x5, a5 = checkpoint(self.sw5, x4, ref_embedding)
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x_out = checkpoint(self.final_lr_conv, x5)
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x_out = checkpoint(self.upsample, x_out)
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x_out = checkpoint(self.final_hr_conv2, x_out)
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if save_attentions:
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self.attentions = [a1, a3, a3, a4, a5]
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return x_out,
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class SimplePyramidMultiplexer(nn.Module):
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def __init__(self, nf, transforms):
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super(SimplePyramidMultiplexer, self).__init__()
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# Blocks used to create the query
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reductions = 3
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self.input_process = ConvGnSilu(nf, nf, activation=True, norm=False, bias=True)
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self.reduction_blocks = nn.ModuleList([HalvingProcessingBlock(int(nf * 1.5 ** i), factor=1.5)
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for i in range(reductions)])
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reduction_filters = int(nf * 1.5 ** reductions)
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self.processing_blocks = nn.Sequential(
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ConvGnSilu(reduction_filters, reduction_filters, kernel_size=3, activation=True, norm=True, bias=False),
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ConvGnSilu(reduction_filters, reduction_filters, kernel_size=3, activation=True, norm=True, bias=False))
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self.expansion_blocks = nn.ModuleList([ExpansionBlock2(int(reduction_filters // (1.5 ** i)), factor=1.5)
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for i in range(reductions)])
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self.cbl1 = ConvGnSilu(nf, nf // 2, kernel_size=1, norm=False, bias=False)
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self.cbl2 = ConvGnSilu(nf // 2, transforms, kernel_size=1, norm=False, bias=False)
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def forward(self, x):
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q = self.input_process(x)
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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(q)
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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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q = self.cbl1(q)
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q = self.cbl2(q)
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return q
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class SimplerSwitchWithReference(nn.Module):
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def __init__(self, nf, num_transforms, init_temperature=10, has_ref=True):
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super(SimplerSwitchWithReference, self).__init__()
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self.nf = nf
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self.transformation_counts = num_transforms
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multiplx_fn = functools.partial(SimplePyramidMultiplexer, nf)
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pretransform = functools.partial(ConvGnLelu, nf, int(nf*1.5), kernel_size=3, bias=False, norm=False, activation=True, weight_init_factor=.1)
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transform_fn = functools.partial(ConvGnLelu, int(nf * 1.5), int(nf * 1.5), kernel_size=3, bias=False, norm=False, activation=True, weight_init_factor=.1)
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posttransform = ConvGnLelu(int(nf*1.5), nf, kernel_size=3, bias=False, norm=False, activation=True, weight_init_factor=.1)
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if has_ref:
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self.ref_join = ReferenceJoinBlock(nf, residual_weight_init_factor=.3, final_norm=False, kernel_size=1, depth=2)
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else:
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self.ref_join = None
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self.switch = ConfigurableSwitchComputer(nf, multiplx_fn,
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pre_transform_block=pretransform, transform_block=transform_fn,
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post_transform_block=posttransform,
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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=False)
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def forward(self, x, ref=None):
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if self.ref_join is not None:
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branch, ref_std = self.ref_join(x, ref)
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return self.switch(branch, identity=x) + (ref_std,)
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else:
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return self.switch(x, identity=x)
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class SsgSimpler(SwitchModelBase):
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def __init__(self, in_nc, out_nc, nf, xforms=8, init_temperature=10, recurrent=False):
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super(SsgSimpler, self).__init__(init_temperature, 10000)
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self.nf = nf
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# processing the input embedding
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if recurrent:
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self.recurrent = True
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self.recurrent_process = ConvGnLelu(in_nc, nf, kernel_size=3, stride=2, norm=False, bias=True, activation=False)
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self.recurrent_join = ReferenceJoinBlock(nf, residual_weight_init_factor=.01, final_norm=False, kernel_size=1, depth=3, join=False)
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else:
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self.recurrent = False
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# Feature branch
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self.model_fea_conv = ConvGnLelu(in_nc, nf, kernel_size=7, norm=False, activation=False)
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self.sw1 = SimplerSwitchWithReference(nf, xforms, init_temperature, has_ref=False)
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self.sw2 = SimplerSwitchWithReference(nf, xforms, init_temperature, has_ref=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=7, norm=False, activation=False, bias=False)
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self.sw_grad = SimplerSwitchWithReference(nf, xforms // 2, init_temperature, has_ref=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 = UpconvBlock(nf, nf // 2, block=ConvGnLelu, norm=False, activation=True, bias=False)
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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.conjoin_sw = SimplerSwitchWithReference(nf, xforms, init_temperature, has_ref=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 = UpconvBlock(nf, nf // 2, block=ConvGnLelu, norm=False, activation=True, bias=True)
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self.final_hr_conv1 = ConvGnLelu(nf // 2, nf // 2, kernel_size=3, norm=False, activation=False, bias=True)
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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.switch, self.sw2.switch, self.sw_grad.switch, self.conjoin_sw.switch]
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def forward(self, x, save_attentions=True, recurrent=None):
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# The attention_maps debugger outputs <x>. Save that here.
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self.lr = x.detach().cpu()
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# If we're not saving attention, we also shouldn't be updating the attention norm. This is because the attention
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# norm should only be getting updates with new data, not recurrent generator sampling.
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for sw in self.switches:
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sw.set_update_attention_norm(save_attentions)
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x1 = self.model_fea_conv(x)
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if self.recurrent:
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rec = self.recurrent_process(recurrent)
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x1, recurrent_std = self.recurrent_join(x1, rec)
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x1, a1 = checkpoint(self.sw1, x1)
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x2, a2 = checkpoint(self.sw2, x1)
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x_grad = self.get_g_nopadding(x)
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x_grad = self.grad_conv(x_grad)
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x_grad, a3, grad_fea_std = checkpoint(self.sw_grad, x_grad, x1)
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x_grad = checkpoint(self.grad_lr_conv, x_grad)
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x_grad_out = checkpoint(self.upsample_grad, x_grad)
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x_grad_out = checkpoint(self.grad_branch_output_conv, x_grad_out)
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x3, a4, fea_grad_std = checkpoint(self.conjoin_sw, x2, x_grad)
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x_out = checkpoint(self.final_lr_conv, x3)
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x_out = checkpoint(self.upsample, x_out)
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x_out = checkpoint(self.final_hr_conv2, x_out)
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if save_attentions:
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self.attentions = [a1, a2, a3, a4]
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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 |