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SPSR4

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aka - return of the backbone! I'm tired of massively overparameterized generators
with pile-of-shit multiplexers. Let's give this another try..
This commit is contained in:
James Betker 2020-09-11 22:55:37 -06:00
parent 19896abaea
commit 4e44bca611
4 changed files with 243 additions and 2 deletions
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133
codes/models/archs/SPSR_arch.py
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@ -5,7 +5,7 @@ import torch.nn.functional as F
from models.archs import SPSR_util as B
from .RRDBNet_arch import RRDB
from models.archs.arch_util import ConvGnLelu, UpconvBlock, ConjoinBlock, ConvGnSilu, MultiConvBlock, ReferenceJoinBlock
from models.archs.SwitchedResidualGenerator_arch import ConvBasisMultiplexer, ConfigurableSwitchComputer, ReferencingConvMultiplexer, ReferenceImageBranch, AdaInConvBlock, ProcessingBranchWithStochasticity
from models.archs.SwitchedResidualGenerator_arch import ConvBasisMultiplexer, ConfigurableSwitchComputer, ReferencingConvMultiplexer, ReferenceImageBranch, AdaInConvBlock, ProcessingBranchWithStochasticity, EmbeddingMultiplexer
from switched_conv_util import save_attention_to_image_rgb
from switched_conv import compute_attention_specificity
import functools
@ -409,3 +409,134 @@ class SwitchedSpsrWithRef2(nn.Module):
val["switch_%i_specificity" % (i,)] = means[i]
val["switch_%i_histogram" % (i,)] = hists[i]
return val
class Spsr4(nn.Module):
def __init__(self, in_nc, out_nc, nf, xforms=8, upscale=4, init_temperature=10):
super(Spsr4, self).__init__()
n_upscale = int(math.log(upscale, 2))
# switch options
transformation_filters = nf
self.transformation_counts = xforms
multiplx_fn = functools.partial(EmbeddingMultiplexer, transformation_filters)
pretransform_fn = functools.partial(ConvGnLelu, transformation_filters, transformation_filters, norm=False, bias=False, weight_init_factor=.1)
transform_fn = functools.partial(MultiConvBlock, transformation_filters, int(transformation_filters * 1.5),
transformation_filters, kernel_size=3, depth=3,
weight_init_factor=.1)
# Feature branch
self.model_fea_conv = ConvGnLelu(in_nc, nf, kernel_size=3, norm=False, activation=False)
self.noise_ref_join = ReferenceJoinBlock(nf, residual_weight_init_factor=.1)
self.sw1 = ConfigurableSwitchComputer(transformation_filters, multiplx_fn,
pre_transform_block=pretransform_fn, transform_block=transform_fn,
attention_norm=True,
transform_count=self.transformation_counts, init_temp=init_temperature,
add_scalable_noise_to_transforms=False)
self.sw2 = ConfigurableSwitchComputer(transformation_filters, multiplx_fn,
pre_transform_block=pretransform_fn, transform_block=transform_fn,
attention_norm=True,
transform_count=self.transformation_counts, init_temp=init_temperature,
add_scalable_noise_to_transforms=False)
self.feature_lr_conv = ConvGnLelu(nf, nf, kernel_size=3, norm=True, activation=False)
self.feature_lr_conv2 = ConvGnLelu(nf, nf, kernel_size=3, norm=False, activation=False, bias=False)
# Grad branch. Note - groupnorm on this branch is REALLY bad. Avoid it like the plague.
self.get_g_nopadding = ImageGradientNoPadding()
self.grad_conv = ConvGnLelu(in_nc, nf, kernel_size=3, norm=False, activation=False, bias=False)
self.noise_ref_join_grad = ReferenceJoinBlock(nf, residual_weight_init_factor=.1)
self.grad_ref_join = ReferenceJoinBlock(nf, residual_weight_init_factor=.3, final_norm=False)
self.sw_grad = ConfigurableSwitchComputer(transformation_filters, multiplx_fn,
pre_transform_block=pretransform_fn, transform_block=transform_fn,
attention_norm=True,
transform_count=self.transformation_counts // 2, init_temp=init_temperature,
add_scalable_noise_to_transforms=False)
self.grad_lr_conv = ConvGnLelu(nf, nf, kernel_size=3, norm=False, activation=True, bias=True)
self.grad_lr_conv2 = ConvGnLelu(nf, nf, kernel_size=3, norm=False, activation=True, bias=True)
self.upsample_grad = nn.Sequential(*[UpconvBlock(nf, nf, block=ConvGnLelu, norm=False, activation=True, bias=False) for _ in range(n_upscale)])
self.grad_branch_output_conv = ConvGnLelu(nf, out_nc, kernel_size=1, norm=False, activation=False, bias=True)
# Join branch (grad+fea)
self.noise_ref_join_conjoin = ReferenceJoinBlock(nf, residual_weight_init_factor=.1)
self.conjoin_ref_join = ReferenceJoinBlock(nf, residual_weight_init_factor=.3)
self.conjoin_sw = ConfigurableSwitchComputer(transformation_filters, multiplx_fn,
pre_transform_block=pretransform_fn, transform_block=transform_fn,
attention_norm=True,
transform_count=self.transformation_counts, init_temp=init_temperature,
add_scalable_noise_to_transforms=False)
self.final_lr_conv = ConvGnLelu(nf, nf, kernel_size=3, norm=False, activation=True, bias=True)
self.upsample = nn.Sequential(*[UpconvBlock(nf, nf, block=ConvGnLelu, norm=False, activation=True, bias=True) for _ in range(n_upscale)])
self.final_hr_conv1 = ConvGnLelu(nf, nf, kernel_size=3, norm=False, activation=False, bias=True)
self.final_hr_conv2 = ConvGnLelu(nf, out_nc, kernel_size=3, norm=False, activation=False, bias=False)
self.switches = [self.sw1, self.sw2, self.sw_grad, self.conjoin_sw]
self.attentions = None
self.init_temperature = init_temperature
self.final_temperature_step = 10000
def forward(self, x, embedding):
noise_stds = []
x_grad = self.get_g_nopadding(x)
x = self.model_fea_conv(x)
x1 = x
x1, a1 = self.sw1(x1, True, identity=x, att_in=(x1, embedding))
x2 = x1
x2, nstd = self.noise_ref_join(x2, torch.randn_like(x2))
x2, a2 = self.sw2(x2, True, identity=x1, att_in=(x2, embedding))
noise_stds.append(nstd)
x_grad = self.grad_conv(x_grad)
x_grad_identity = x_grad
x_grad, nstd = self.noise_ref_join_grad(x_grad, torch.randn_like(x_grad))
x_grad, grad_fea_std = self.grad_ref_join(x_grad, x1)
x_grad, a3 = self.sw_grad(x_grad, True, identity=x_grad_identity, att_in=(x_grad, embedding))
x_grad = self.grad_lr_conv(x_grad)
x_grad = self.grad_lr_conv2(x_grad)
x_grad_out = self.upsample_grad(x_grad)
x_grad_out = self.grad_branch_output_conv(x_grad_out)
noise_stds.append(nstd)
x_out = x2
x_out, nstd = self.noise_ref_join_conjoin(x_out, torch.randn_like(x_out))
x_out, fea_grad_std = self.conjoin_ref_join(x_out, x_grad)
x_out, a4 = self.conjoin_sw(x_out, True, identity=x2, att_in=(x_out, embedding))
x_out = self.final_lr_conv(x_out)
x_out = self.upsample(x_out)
x_out = self.final_hr_conv1(x_out)
x_out = self.final_hr_conv2(x_out)
noise_stds.append(nstd)
self.attentions = [a1, a2, a3, a4]
self.noise_stds = torch.stack(noise_stds).mean().detach().cpu()
self.grad_fea_std = grad_fea_std.detach().cpu()
self.fea_grad_std = fea_grad_std.detach().cpu()
return x_grad_out, x_out, x_grad
def set_temperature(self, temp):
[sw.set_temperature(temp) for sw in self.switches]
def update_for_step(self, step, experiments_path='.'):
if self.attentions:
temp = max(1, 1 + self.init_temperature *
(self.final_temperature_step - step) / self.final_temperature_step)
self.set_temperature(temp)
if step % 200 == 0:
output_path = os.path.join(experiments_path, "attention_maps", "a%i")
prefix = "attention_map_%i_%%i.png" % (step,)
[save_attention_to_image_rgb(output_path % (i,), self.attentions[i], self.transformation_counts, prefix, step) for i in range(len(self.attentions))]
def get_debug_values(self, step):
temp = self.switches[0].switch.temperature
mean_hists = [compute_attention_specificity(att, 2) for att in self.attentions]
means = [i[0] for i in mean_hists]
hists = [i[1].clone().detach().cpu().flatten() for i in mean_hists]
val = {"switch_temperature": temp,
"noise_branch_std_dev": self.noise_stds,
"grad_branch_feat_intg_std_dev": self.grad_fea_std,
"conjoin_branch_grad_intg_std_dev": self.fea_grad_std}
for i in range(len(means)):
val["switch_%i_specificity" % (i,)] = means[i]
val["switch_%i_histogram" % (i,)] = hists[i]
return val

104
codes/models/archs/SwitchedResidualGenerator_arch.py
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@ -8,6 +8,7 @@ from models.archs.arch_util import ConvBnLelu, ConvGnSilu, ExpansionBlock, Expan
from switched_conv_util import save_attention_to_image_rgb
import os
from torch.utils.checkpoint import checkpoint
from models.archs.spinenet_arch import SpineNet
# Set to true to relieve memory pressure by using torch.utils.checkpoint in several memory-critical locations.
@ -335,3 +336,106 @@ class ConfigurableSwitchedResidualGenerator2(nn.Module):
val["switch_%i_histogram" % (i,)] = hists[i]
return val
# This class encapsulates an encoder based on an object detection network backbone whose purpose is to generated a
# structured embedding encoding what is in an image patch. This embedding can then be used to perform structured
# alterations to the underlying image.
#
# Caveat: Since this uses a pre-defined (and potentially pre-trained) SpineNet backbone, it has a minimum-supported
# image size, which is 128x128. In order to use 64x64 patches, you must set interpolate_first=True. though this will
# degrade quality.
class BackboneEncoder(nn.Module):
def __init__(self, interpolate_first=True, pretrained_backbone=None):
super(BackboneEncoder, self).__init__()
self.interpolate_first = interpolate_first
# Uses dual spinenets, one for the input patch and the other for the reference image.
self.patch_spine = SpineNet('49', in_channels=3, use_input_norm=True)
self.ref_spine = SpineNet('49', in_channels=3, use_input_norm=True)
self.merge_process1 = ConvGnSilu(512, 512, kernel_size=1, activation=True, norm=False, bias=True)
self.merge_process2 = ConvGnSilu(512, 384, kernel_size=1, activation=True, norm=True, bias=False)
self.merge_process3 = ConvGnSilu(384, 256, kernel_size=1, activation=False, norm=False, bias=True)
if pretrained_backbone is not None:
loaded_params = torch.load(pretrained_backbone)
self.ref_spine.load_state_dict(loaded_params['state_dict'], strict=True)
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, ref, ref_center_point):
if self.interpolate_first:
x = F.interpolate(x, scale_factor=2, mode="bicubic")
# Don't interpolate ref - assume it is fed in at the proper resolution.
# ref = F.interpolate(ref, scale_factor=2, mode="bicubic")
# [ref] will have a 'mask' channel which we cannot use with pretrained spinenet.
ref = ref[:, :3, :, :]
ref_emb = checkpoint(self.ref_spine, ref)[0]
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.ref_spine, x)[0]
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))
combined = self.merge_process2(combined)
combined = self.merge_process3(combined)
return combined
# 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
if __name__ == '__main__':
bb = BackboneEncoder(64)
emb = EmbeddingMultiplexer(64, 10)
x = torch.randn(4,3,64,64)
r = torch.randn(4,4,64,64)
xu = torch.randn(4,64,64,64)
cp = torch.zeros((4,2), dtype=torch.long)
b = bb(x, r, cp)
emb(xu, b)

2
codes/models/archs/spinenet_arch.py
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@ -365,4 +365,4 @@ class SpineNet(nn.Module):
if spec.is_output:
output_feat[spec.level] = target_feat
return [self.endpoint_convs[str(level)](output_feat[level]) for level in self.output_level]
return tuple([self.endpoint_convs[str(level)](output_feat[level]) for level in self.output_level])

6
codes/models/networks.py
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@ -51,6 +51,12 @@ def define_G(opt, net_key='network_G', scale=None):
xforms = opt_net['num_transforms'] if 'num_transforms' in opt_net.keys() else 8
netG = spsr.SwitchedSpsrWithRef2(in_nc=3, out_nc=3, nf=opt_net['nf'], xforms=xforms, upscale=opt_net['scale'],
init_temperature=opt_net['temperature'] if 'temperature' in opt_net.keys() else 10)
elif which_model == "spsr4":
xforms = opt_net['num_transforms'] if 'num_transforms' in opt_net.keys() else 8
netG = spsr.Spsr4(in_nc=3, out_nc=3, nf=opt_net['nf'], xforms=xforms, upscale=opt_net['scale'],
init_temperature=opt_net['temperature'] if 'temperature' in opt_net.keys() else 10)
elif which_model == "backbone_encoder":
netG = SwitchedGen_arch.BackboneEncoder(pretrained_backbone=opt_net['pretrained_spinenet'])
else:
raise NotImplementedError('Generator model [{:s}] not recognized'.format(which_model))