forked from mrq/DL-Art-School
574 lines
28 KiB
Python
574 lines
28 KiB
Python
import torch
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from torch import nn
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from switched_conv import BareConvSwitch, compute_attention_specificity, AttentionNorm
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import torch.nn.functional as F
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import functools
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from collections import OrderedDict
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from models.archs.arch_util import ConvBnLelu, ConvGnSilu, ExpansionBlock, ExpansionBlock2, ConvGnLelu, MultiConvBlock, SiLU
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from switched_conv_util import save_attention_to_image_rgb
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import os
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from torch.utils.checkpoint import checkpoint
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from models.archs.spinenet_arch import SpineNet
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# Set to true to relieve memory pressure by using torch.utils.checkpoint in several memory-critical locations.
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memory_checkpointing_enabled = True
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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, 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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# This is a classic u-net architecture with the goal of assigning each individual pixel an individual transform
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# switching set.
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class ConvBasisMultiplexer(nn.Module):
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def __init__(self, input_channels, base_filters, reductions, processing_depth, multiplexer_channels, use_gn=True, use_exp2=False):
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super(ConvBasisMultiplexer, self).__init__()
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self.filter_conv = ConvGnSilu(input_channels, base_filters, bias=True)
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self.reduction_blocks = nn.ModuleList([HalvingProcessingBlock(base_filters * 2 ** i) for i in range(reductions)])
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reduction_filters = base_filters * 2 ** reductions
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self.processing_blocks = nn.Sequential(OrderedDict([('block%i' % (i,), ConvGnSilu(reduction_filters, reduction_filters, bias=False)) for i in range(processing_depth)]))
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if use_exp2:
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self.expansion_blocks = nn.ModuleList([ExpansionBlock2(reduction_filters // (2 ** i)) for i in range(reductions)])
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else:
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self.expansion_blocks = nn.ModuleList([ExpansionBlock(reduction_filters // (2 ** i)) for i in range(reductions)])
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gap = base_filters - multiplexer_channels
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cbl1_out = ((base_filters - (gap // 2)) // 4) * 4 # Must be multiples of 4 to use with group norm.
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self.cbl1 = ConvGnSilu(base_filters, cbl1_out, norm=use_gn, bias=False, num_groups=4)
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cbl2_out = ((base_filters - (3 * gap // 4)) // 4) * 4
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self.cbl2 = ConvGnSilu(cbl1_out, cbl2_out, norm=use_gn, bias=False, num_groups=4)
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self.cbl3 = ConvGnSilu(cbl2_out, multiplexer_channels, bias=True, norm=False)
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def forward(self, x):
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x = self.filter_conv(x)
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reduction_identities = []
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for b in self.reduction_blocks:
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reduction_identities.append(x)
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x = b(x)
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x = self.processing_blocks(x)
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for i, b in enumerate(self.expansion_blocks):
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x = b(x, reduction_identities[-i - 1])
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x = self.cbl1(x)
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x = self.cbl2(x)
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x = self.cbl3(x)
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return x
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# torch.gather() which operates across 2d images.
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def gather_2d(input, index):
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b, c, h, w = input.shape
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nodim = input.view(b, c, h * w)
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ind_nd = index[:, 0]*w + index[:, 1]
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ind_nd = ind_nd.unsqueeze(1)
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ind_nd = ind_nd.repeat((1, c))
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ind_nd = ind_nd.unsqueeze(2)
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result = torch.gather(nodim, dim=2, index=ind_nd)
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result = result.squeeze()
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if b == 1:
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result = result.unsqueeze(0)
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return result
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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 * 8.
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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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self.filter_conv = ConvGnSilu(4, base_filters, bias=True)
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self.reduction_blocks = nn.ModuleList([HalvingProcessingBlock(base_filters * 2 ** i) for i in range(3)])
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reduction_filters = base_filters * 2 ** 3
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self.processing_blocks = nn.Sequential(OrderedDict([('block%i' % (i,), ConvGnSilu(reduction_filters, reduction_filters, bias=False)) for i in range(4)]))
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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.filter_conv(x)
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reduction_identities = []
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for b in self.reduction_blocks:
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reduction_identities.append(x)
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x = b(x)
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x = self.processing_blocks(x)
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return gather_2d(x, center_point // 8)
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class AdaInConvBlock(nn.Module):
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def __init__(self, reference_size, in_nc, out_nc, conv_block=ConvGnLelu):
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super(AdaInConvBlock, self).__init__()
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self.filter_conv = conv_block(in_nc, out_nc, activation=True, norm=False, bias=False)
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self.ref_proc = nn.Linear(reference_size, reference_size)
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self.ref_red = nn.Linear(reference_size, out_nc * 2)
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self.feature_norm = torch.nn.InstanceNorm2d(out_nc)
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self.style_norm = torch.nn.InstanceNorm1d(out_nc)
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self.post_fuse_conv = conv_block(out_nc, out_nc, activation=False, norm=True, bias=True)
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def forward(self, x, ref):
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x = self.feature_norm(self.filter_conv(x))
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ref = self.ref_proc(ref)
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ref = self.ref_red(ref)
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b, c = ref.shape
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ref = self.style_norm(ref.view(b, 2, c // 2))
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x = x * ref[:, 0, :].unsqueeze(dim=2).unsqueeze(dim=3).expand(x.shape) + ref[:, 1, :].unsqueeze(dim=2).unsqueeze(dim=3).expand(x.shape)
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return self.post_fuse_conv(x)
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class ProcessingBranchWithStochasticity(nn.Module):
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def __init__(self, nf_in, nf_out, noise_filters, depth):
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super(ProcessingBranchWithStochasticity, self).__init__()
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nf_gap = nf_out - nf_in
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self.noise_filters = noise_filters
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self.processor = MultiConvBlock(nf_in + noise_filters, nf_in + nf_gap // 2, nf_out, kernel_size=3, depth=depth, weight_init_factor = .1)
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def forward(self, x):
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b, c, h, w = x.shape
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noise = torch.randn((b, self.noise_filters, h, w), device=x.device)
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return self.processor(torch.cat([x, noise], dim=1))
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# This is similar to ConvBasisMultiplexer, except that it takes a linear reference tensor as a second input to
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# provide better results. It also has fixed parameterization in several places
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class ReferencingConvMultiplexer(nn.Module):
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def __init__(self, input_channels, base_filters, multiplexer_channels, use_gn=True):
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super(ReferencingConvMultiplexer, self).__init__()
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self.style_fuse = AdaInConvBlock(512, input_channels, base_filters, ConvGnSilu)
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self.reduction_blocks = nn.ModuleList([HalvingProcessingBlock(base_filters * 2 ** i) for i in range(3)])
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reduction_filters = base_filters * 2 ** 3
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self.processing_blocks = nn.Sequential(OrderedDict([('block%i' % (i,), ConvGnSilu(reduction_filters, reduction_filters, bias=False)) for i in range(2)]))
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self.expansion_blocks = nn.ModuleList([ExpansionBlock2(reduction_filters // (2 ** i)) for i in range(3)])
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gap = base_filters - multiplexer_channels
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cbl1_out = ((base_filters - (gap // 2)) // 4) * 4 # Must be multiples of 4 to use with group norm.
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self.cbl1 = ConvGnSilu(base_filters, cbl1_out, norm=use_gn, bias=False, num_groups=4)
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cbl2_out = ((base_filters - (3 * gap // 4)) // 4) * 4
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self.cbl2 = ConvGnSilu(cbl1_out, cbl2_out, norm=use_gn, bias=False, num_groups=4)
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self.cbl3 = ConvGnSilu(cbl2_out, multiplexer_channels, bias=True, norm=False)
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def forward(self, x, ref):
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x = self.style_fuse(x, ref)
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reduction_identities = []
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for b in self.reduction_blocks:
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reduction_identities.append(x)
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x = b(x)
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x = self.processing_blocks(x)
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for i, b in enumerate(self.expansion_blocks):
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x = b(x, reduction_identities[-i - 1])
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x = self.cbl1(x)
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x = self.cbl2(x)
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x = self.cbl3(x)
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return x
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class ConfigurableSwitchComputer(nn.Module):
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def __init__(self, base_filters, multiplexer_net, pre_transform_block, transform_block, transform_count, attention_norm,
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init_temp=20, add_scalable_noise_to_transforms=False, feed_transforms_into_multiplexer=False):
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super(ConfigurableSwitchComputer, self).__init__()
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tc = transform_count
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self.multiplexer = multiplexer_net(tc)
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if pre_transform_block:
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self.pre_transform = pre_transform_block()
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else:
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self.pre_transform = None
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self.transforms = nn.ModuleList([transform_block() for _ in range(transform_count)])
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self.add_noise = add_scalable_noise_to_transforms
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self.feed_transforms_into_multiplexer = feed_transforms_into_multiplexer
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self.noise_scale = nn.Parameter(torch.full((1,), float(1e-3)))
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# And the switch itself, including learned scalars
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self.switch = BareConvSwitch(initial_temperature=init_temp, attention_norm=AttentionNorm(transform_count, accumulator_size=16 * transform_count) if attention_norm else None)
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self.switch_scale = nn.Parameter(torch.full((1,), float(1)))
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self.post_switch_conv = ConvBnLelu(base_filters, base_filters, norm=False, bias=True)
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# The post_switch_conv gets a low scale initially. The network can decide to magnify it (or not)
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# depending on its needs.
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self.psc_scale = nn.Parameter(torch.full((1,), float(.1)))
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# Regarding inputs: it is acceptable to pass in a tuple/list as an input for (x), but the first element
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# *must* be the actual parameter that gets fed through the network - it is assumed to be the identity.
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def forward(self, x, output_attention_weights=False, identity=None, att_in=None, fixed_scale=1):
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if isinstance(x, tuple):
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x1 = x[0]
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else:
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x1 = x
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if att_in is None:
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att_in = x
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if identity is None:
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identity = x1
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if self.add_noise:
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rand_feature = torch.randn_like(x1) * self.noise_scale
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if isinstance(x, tuple):
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x = (x1 + rand_feature,) + x[1:]
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else:
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x = x1 + rand_feature
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if not isinstance(x, tuple):
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x = (x,)
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if self.pre_transform:
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x = self.pre_transform(*x)
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if not isinstance(x, tuple):
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x = (x,)
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if memory_checkpointing_enabled:
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xformed = [checkpoint(t, *x) for t in self.transforms]
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else:
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xformed = [t(*x) for t in self.transforms]
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if not isinstance(att_in, tuple):
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att_in = (att_in,)
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if self.feed_transforms_into_multiplexer:
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att_in = att_in + (torch.stack(xformed, dim=1),)
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if memory_checkpointing_enabled:
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m = checkpoint(self.multiplexer, *att_in)
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else:
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m = self.multiplexer(*att_in)
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# It is assumed that [xformed] and [m] are collapsed into tensors at this point.
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outputs, attention = self.switch(xformed, m, True)
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outputs = identity + outputs * self.switch_scale * fixed_scale
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outputs = outputs + self.post_switch_conv(outputs) * self.psc_scale * fixed_scale
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if output_attention_weights:
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return outputs, attention
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else:
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return outputs
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def set_temperature(self, temp):
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self.switch.set_attention_temperature(temp)
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class ConfigurableSwitchedResidualGenerator2(nn.Module):
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def __init__(self, switch_depth, switch_filters, switch_reductions, switch_processing_layers, trans_counts, trans_kernel_sizes,
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trans_layers, transformation_filters, attention_norm, initial_temp=20, final_temperature_step=50000, heightened_temp_min=1,
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heightened_final_step=50000, upsample_factor=1,
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add_scalable_noise_to_transforms=False):
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super(ConfigurableSwitchedResidualGenerator2, self).__init__()
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switches = []
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self.initial_conv = ConvBnLelu(3, transformation_filters, norm=False, activation=False, bias=True)
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self.upconv1 = ConvBnLelu(transformation_filters, transformation_filters, norm=False, bias=True)
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self.upconv2 = ConvBnLelu(transformation_filters, transformation_filters, norm=False, bias=True)
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self.hr_conv = ConvBnLelu(transformation_filters, transformation_filters, norm=False, bias=True)
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self.final_conv = ConvBnLelu(transformation_filters, 3, norm=False, activation=False, bias=True)
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for _ in range(switch_depth):
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multiplx_fn = functools.partial(ConvBasisMultiplexer, transformation_filters, switch_filters, switch_reductions, switch_processing_layers, trans_counts)
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pretransform_fn = functools.partial(ConvBnLelu, transformation_filters, transformation_filters, norm=False, bias=False, weight_init_factor=.1)
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transform_fn = functools.partial(MultiConvBlock, transformation_filters, int(transformation_filters * 1.5), transformation_filters, kernel_size=trans_kernel_sizes, depth=trans_layers, weight_init_factor=.1)
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switches.append(ConfigurableSwitchComputer(transformation_filters, multiplx_fn,
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pre_transform_block=pretransform_fn, transform_block=transform_fn,
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attention_norm=attention_norm,
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transform_count=trans_counts, init_temp=initial_temp,
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add_scalable_noise_to_transforms=add_scalable_noise_to_transforms))
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self.switches = nn.ModuleList(switches)
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self.transformation_counts = trans_counts
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self.init_temperature = initial_temp
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self.final_temperature_step = final_temperature_step
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self.heightened_temp_min = heightened_temp_min
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self.heightened_final_step = heightened_final_step
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self.attentions = None
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self.upsample_factor = upsample_factor
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assert self.upsample_factor == 2 or self.upsample_factor == 4
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def forward(self, x):
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# This is a common bug when evaluating SRG2 generators. It needs to be configured properly in eval mode. Just fail.
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if not self.train:
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assert self.switches[0].switch.temperature == 1
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x = self.initial_conv(x)
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self.attentions = []
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for i, sw in enumerate(self.switches):
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x, att = sw.forward(x, True)
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self.attentions.append(att)
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x = self.upconv1(F.interpolate(x, scale_factor=2, mode="nearest"))
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if self.upsample_factor > 2:
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x = F.interpolate(x, scale_factor=2, mode="nearest")
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x = self.upconv2(x)
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x = self.final_conv(self.hr_conv(x))
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return x, x
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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,
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1 + self.init_temperature * (self.final_temperature_step - step) / self.final_temperature_step)
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if temp == 1 and self.heightened_final_step and step > self.final_temperature_step and \
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self.heightened_final_step != 1:
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# Once the temperature passes (1) it enters an inverted curve to match the linear curve from above.
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# without this, the attention specificity "spikes" incredibly fast in the last few iterations.
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h_steps_total = self.heightened_final_step - self.final_temperature_step
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h_steps_current = min(step - self.final_temperature_step, h_steps_total)
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# The "gap" will represent the steps that need to be traveled as a linear function.
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h_gap = 1 / self.heightened_temp_min
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temp = h_gap * h_steps_current / h_steps_total
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# Invert temperature to represent reality on this side of the curve
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temp = 1 / temp
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self.set_temperature(temp)
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if step % 50 == 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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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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# This class encapsulates an encoder based on an object detection network backbone whose purpose is to generated a
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# structured embedding encoding what is in an image patch. This embedding can then be used to perform structured
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# alterations to the underlying image.
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#
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# Caveat: Since this uses a pre-defined (and potentially pre-trained) SpineNet backbone, it has a minimum-supported
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# image size, which is 128x128. In order to use 64x64 patches, you must set interpolate_first=True. though this will
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# degrade quality.
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class BackboneEncoder(nn.Module):
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def __init__(self, interpolate_first=True, pretrained_backbone=None):
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super(BackboneEncoder, self).__init__()
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self.interpolate_first = interpolate_first
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# Uses dual spinenets, one for the input patch and the other for the reference image.
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self.patch_spine = SpineNet('49', in_channels=3, use_input_norm=True)
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self.ref_spine = SpineNet('49', in_channels=3, use_input_norm=True)
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self.merge_process1 = ConvGnSilu(512, 512, kernel_size=1, activation=True, norm=False, bias=True)
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self.merge_process2 = ConvGnSilu(512, 384, kernel_size=1, activation=True, norm=True, bias=False)
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self.merge_process3 = ConvGnSilu(384, 256, kernel_size=1, activation=False, norm=False, bias=True)
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if pretrained_backbone is not None:
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loaded_params = torch.load(pretrained_backbone)
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self.ref_spine.load_state_dict(loaded_params['state_dict'], strict=True)
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self.patch_spine.load_state_dict(loaded_params['state_dict'], strict=True)
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# Returned embedding will have been reduced in size by a factor of 8 (4 if interpolate_first=True).
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# Output channels are always 256.
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# ex, 64x64 input with interpolate_first=True will result in tensor of shape [bx256x16x16]
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def forward(self, x, ref, ref_center_point):
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if self.interpolate_first:
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x = F.interpolate(x, scale_factor=2, mode="bicubic")
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# Don't interpolate ref - assume it is fed in at the proper resolution.
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# ref = F.interpolate(ref, scale_factor=2, mode="bicubic")
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# [ref] will have a 'mask' channel which we cannot use with pretrained spinenet.
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ref = ref[:, :3, :, :]
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|
ref_emb = checkpoint(self.ref_spine, ref)[0]
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ref_code = gather_2d(ref_emb, ref_center_point // 8) # Divide by 8 to bring the center point to the correct location.
|
|
|
|
patch = checkpoint(self.patch_spine, x)[0]
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|
ref_code_expanded = ref_code.view(-1, 256, 1, 1).repeat(1, 1, patch.shape[2], patch.shape[3])
|
|
combined = self.merge_process1(torch.cat([patch, ref_code_expanded], dim=1))
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|
combined = self.merge_process2(combined)
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|
combined = self.merge_process3(combined)
|
|
|
|
return combined
|
|
|
|
|
|
class BackboneEncoderNoRef(nn.Module):
|
|
def __init__(self, interpolate_first=True, pretrained_backbone=None):
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|
super(BackboneEncoderNoRef, self).__init__()
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|
self.interpolate_first = interpolate_first
|
|
|
|
self.patch_spine = SpineNet('49', in_channels=3, use_input_norm=True)
|
|
|
|
if pretrained_backbone is not None:
|
|
loaded_params = torch.load(pretrained_backbone)
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|
self.patch_spine.load_state_dict(loaded_params['state_dict'], strict=True)
|
|
|
|
# Returned embedding will have been reduced in size by a factor of 8 (4 if interpolate_first=True).
|
|
# Output channels are always 256.
|
|
# ex, 64x64 input with interpolate_first=True will result in tensor of shape [bx256x16x16]
|
|
def forward(self, x):
|
|
if self.interpolate_first:
|
|
x = F.interpolate(x, scale_factor=2, mode="bicubic")
|
|
|
|
patch = checkpoint(self.patch_spine, x)[0]
|
|
return patch
|
|
|
|
|
|
class BackboneSpinenetNoHead(nn.Module):
|
|
def __init__(self):
|
|
super(BackboneSpinenetNoHead, self).__init__()
|
|
self.patch_spine = SpineNet('49', in_channels=3, use_input_norm=True)
|
|
|
|
def forward(self, x):
|
|
patch = checkpoint(self.patch_spine, x)[0]
|
|
return patch
|
|
|
|
|
|
class ResBlock(nn.Module):
|
|
def __init__(self, nf, downsample):
|
|
super(ResBlock, self).__init__()
|
|
nf_int = nf * 2
|
|
nf_out = nf * 2 if downsample else nf
|
|
stride = 2 if downsample else 1
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|
self.c1 = ConvGnSilu(nf, nf_int, kernel_size=3, bias=False, activation=True, norm=True)
|
|
self.c2 = ConvGnSilu(nf_int, nf_int, stride=stride, kernel_size=3, bias=False, activation=True, norm=True)
|
|
self.c3 = ConvGnSilu(nf_int, nf_out, kernel_size=3, bias=False, activation=False, norm=True)
|
|
if downsample:
|
|
self.downsample = ConvGnSilu(nf, nf_out, kernel_size=1, stride=stride, bias=False, activation=False, norm=True)
|
|
else:
|
|
self.downsample = None
|
|
self.act = SiLU(inplace=True)
|
|
|
|
def forward(self, x):
|
|
identity = x
|
|
branch = self.c1(x)
|
|
branch = self.c2(branch)
|
|
branch = self.c3(branch)
|
|
|
|
if self.downsample:
|
|
identity = self.downsample(identity)
|
|
return self.act(identity + branch)
|
|
|
|
|
|
class BackboneResnet(nn.Module):
|
|
def __init__(self):
|
|
super(BackboneResnet, self).__init__()
|
|
self.initial_conv = ConvGnSilu(3, 64, kernel_size=7, bias=True, activation=False, norm=False)
|
|
self.sequence = nn.Sequential(
|
|
ResBlock(64, downsample=False),
|
|
ResBlock(64, downsample=True),
|
|
ResBlock(128, downsample=False),
|
|
ResBlock(128, downsample=True),
|
|
ResBlock(256, downsample=False),
|
|
ResBlock(256, downsample=False))
|
|
|
|
def forward(self, x):
|
|
fea = self.initial_conv(x)
|
|
return self.sequence(fea)
|
|
|
|
|
|
# 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.
|
|
#
|
|
# Implemented as basically a u-net which reduces the input into the same structural space as the embedding, combines the
|
|
# two, then expands back into the original feature space.
|
|
class EmbeddingMultiplexer(nn.Module):
|
|
# Note: reductions=2 if the encoder is using interpolated input, otherwise reductions=3.
|
|
def __init__(self, nf, multiplexer_channels, reductions=2):
|
|
super(EmbeddingMultiplexer, self).__init__()
|
|
self.embedding_process = MultiConvBlock(256, 256, 256, kernel_size=3, depth=3, norm=True)
|
|
|
|
self.filter_conv = 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 + 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)])
|
|
|
|
gap = nf - multiplexer_channels
|
|
cbl1_out = ((nf - (gap // 2)) // 4) * 4 # Must be multiples of 4 to use with group norm.
|
|
self.cbl1 = ConvGnSilu(nf, cbl1_out, norm=True, bias=False, num_groups=4)
|
|
cbl2_out = ((nf - (3 * gap // 4)) // 4) * 4
|
|
self.cbl2 = ConvGnSilu(cbl1_out, cbl2_out, norm=True, bias=False, num_groups=4)
|
|
self.cbl3 = ConvGnSilu(cbl2_out, multiplexer_channels, bias=True, norm=False)
|
|
|
|
def forward(self, x, embedding):
|
|
x = self.filter_conv(x)
|
|
embedding = self.embedding_process(embedding)
|
|
|
|
reduction_identities = []
|
|
for b in self.reduction_blocks:
|
|
reduction_identities.append(x)
|
|
x = b(x)
|
|
x = self.processing_blocks(torch.cat([x, embedding], dim=1))
|
|
for i, b in enumerate(self.expansion_blocks):
|
|
x = b(x, reduction_identities[-i - 1])
|
|
|
|
x = self.cbl1(x)
|
|
x = self.cbl2(x)
|
|
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.query_key_combine = ConvGnSilu(nf*2, nf, kernel_size=1, activation=True, norm=False, bias=False)
|
|
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)
|
|
|
|
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)
|
|
|
|
|
|
if __name__ == '__main__':
|
|
bb = BackboneEncoder(64)
|
|
emb = QueryKeyMultiplexer(64, 10)
|
|
x = torch.randn(4,3,64,64)
|
|
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, trans) |