forked from mrq/DL-Art-School
spsr6
This is meant to be a variant of SPSR5 that harkens back to the simpler earlier architectures that do not have embeddings or ref_ inputs, but do have deep multiplexers. It does, however, use some of the new conjoin mechanisms.
This commit is contained in:
James Betker
parent
7e240f2fed
commit
db52bec4ab
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@ -5,9 +5,10 @@ import torch.nn.functional as F
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from models.archs import SPSR_util as B
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from .RRDBNet_arch import RRDB
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from models.archs.arch_util import ConvGnLelu, UpconvBlock, ConjoinBlock, ConvGnSilu, MultiConvBlock, ReferenceJoinBlock
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from models.archs.SwitchedResidualGenerator_arch import ConvBasisMultiplexer, ConfigurableSwitchComputer, ReferencingConvMultiplexer, ReferenceImageBranch, AdaInConvBlock, ProcessingBranchWithStochasticity, EmbeddingMultiplexer, QueryKeyMultiplexer
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from models.archs.SwitchedResidualGenerator_arch import ConvBasisMultiplexer, ConfigurableSwitchComputer, ReferencingConvMultiplexer, ReferenceImageBranch, AdaInConvBlock, ProcessingBranchWithStochasticity, EmbeddingMultiplexer, QueryKeyMultiplexer, QueryKeyPyramidMultiplexer
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from switched_conv_util import save_attention_to_image_rgb
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from switched_conv import compute_attention_specificity
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from torch.utils.checkpoint import checkpoint
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import functools
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import os
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import torchvision
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@ -812,3 +813,126 @@ class Spsr5(nn.Module):
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return val
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class Spsr6(nn.Module):
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def __init__(self, in_nc, out_nc, nf, xforms=8, upscale=4, multiplexer_reductions=3, init_temperature=10):
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super(Spsr6, self).__init__()
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n_upscale = int(math.log(upscale, 2))
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# switch options
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transformation_filters = nf
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self.transformation_counts = xforms
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multiplx_fn = functools.partial(QueryKeyPyramidMultiplexer, transformation_filters, reductions=multiplexer_reductions)
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transform_fn = functools.partial(MultiConvBlock, transformation_filters, int(transformation_filters * 1.5),
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transformation_filters, kernel_size=3, depth=3,
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weight_init_factor=.1)
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# Feature branch
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self.model_fea_conv = ConvGnLelu(in_nc, nf, kernel_size=7, norm=False, activation=False)
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self.sw1 = ConfigurableSwitchComputer(transformation_filters, multiplx_fn,
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pre_transform_block=None, transform_block=transform_fn,
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attention_norm=True,
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transform_count=self.transformation_counts, init_temp=init_temperature,
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add_scalable_noise_to_transforms=False, feed_transforms_into_multiplexer=True)
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self.sw2 = ConfigurableSwitchComputer(transformation_filters, multiplx_fn,
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pre_transform_block=None, transform_block=transform_fn,
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attention_norm=True,
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transform_count=self.transformation_counts, init_temp=init_temperature,
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add_scalable_noise_to_transforms=False, feed_transforms_into_multiplexer=True)
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self.feature_lr_conv = ConvGnLelu(nf, nf, kernel_size=3, norm=True, activation=False)
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self.feature_lr_conv2 = ConvGnLelu(nf, nf, kernel_size=3, norm=False, activation=False, bias=False)
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# Grad branch. Note - groupnorm on this branch is REALLY bad. Avoid it like the plague.
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self.get_g_nopadding = ImageGradientNoPadding()
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self.grad_conv = ConvGnLelu(in_nc, nf, kernel_size=3, norm=False, activation=False, bias=False)
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self.grad_ref_join = ReferenceJoinBlock(nf, residual_weight_init_factor=.3, final_norm=False)
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self.sw_grad = ConfigurableSwitchComputer(transformation_filters, multiplx_fn,
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pre_transform_block=None, transform_block=transform_fn,
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attention_norm=True,
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transform_count=self.transformation_counts // 2, init_temp=init_temperature,
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add_scalable_noise_to_transforms=False, feed_transforms_into_multiplexer=True)
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self.grad_lr_conv = ConvGnLelu(nf, nf, kernel_size=3, norm=False, activation=True, bias=True)
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self.grad_lr_conv2 = ConvGnLelu(nf, nf, kernel_size=1, norm=False, activation=True, bias=True)
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self.upsample_grad = nn.Sequential(*[UpconvBlock(nf, nf, block=ConvGnLelu, norm=False, activation=True, bias=False) for _ in range(n_upscale)])
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self.grad_branch_output_conv = ConvGnLelu(nf, out_nc, kernel_size=1, norm=False, activation=False, bias=True)
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# Join branch (grad+fea)
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self.noise_ref_join_conjoin = ReferenceJoinBlock(nf, residual_weight_init_factor=.1)
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self.conjoin_ref_join = ReferenceJoinBlock(nf, residual_weight_init_factor=.3)
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self.conjoin_sw = ConfigurableSwitchComputer(transformation_filters, multiplx_fn,
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pre_transform_block=None, transform_block=transform_fn,
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attention_norm=True,
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transform_count=self.transformation_counts, init_temp=init_temperature,
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add_scalable_noise_to_transforms=False, feed_transforms_into_multiplexer=True)
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self.final_lr_conv = ConvGnLelu(nf, nf, kernel_size=3, norm=False, activation=True, bias=True)
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self.upsample = nn.Sequential(*[UpconvBlock(nf, nf, block=ConvGnLelu, norm=False, activation=True, bias=True) for _ in range(n_upscale)])
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self.final_hr_conv1 = ConvGnLelu(nf, nf, kernel_size=3, norm=False, activation=False, bias=True)
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self.final_hr_conv2 = ConvGnLelu(nf, out_nc, kernel_size=1, norm=False, activation=False, bias=False)
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self.switches = [self.sw1, self.sw2, self.sw_grad, self.conjoin_sw]
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self.attentions = None
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self.init_temperature = init_temperature
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self.final_temperature_step = 10000
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self.lr = None
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def forward(self, x):
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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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x_grad = self.get_g_nopadding(x)
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x = self.model_fea_conv(x)
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x1 = x
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x1, a1 = self.sw1(x1, True, identity=x)
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x2 = x1
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x2, a2 = self.sw2(x2, True, identity=x1)
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x_grad = self.grad_conv(x_grad)
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x_grad_identity = x_grad
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x_grad, grad_fea_std = self.grad_ref_join(x_grad, x1)
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x_grad, a3 = self.sw_grad(x_grad, True, identity=x_grad_identity)
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x_grad = self.grad_lr_conv(x_grad)
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x_grad = self.grad_lr_conv2(x_grad)
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x_grad_out = self.upsample_grad(x_grad)
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x_grad_out = self.grad_branch_output_conv(x_grad_out)
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x_out = x2
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x_out, fea_grad_std = self.conjoin_ref_join(x_out, x_grad)
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x_out, a4 = self.conjoin_sw(x_out, True, identity=x2)
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x_out = 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_conv1, x_out)
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x_out = self.final_hr_conv2(x_out)
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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, x_grad
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def set_temperature(self, temp):
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[sw.set_temperature(temp) for sw in self.switches]
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def update_for_step(self, step, experiments_path='.'):
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if self.attentions:
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temp = max(1, 1 + self.init_temperature *
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(self.final_temperature_step - step) / self.final_temperature_step)
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self.set_temperature(temp)
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if step % 500 == 0:
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output_path = os.path.join(experiments_path, "attention_maps")
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prefix = "amap_%i_a%i_%%i.png"
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[save_attention_to_image_rgb(output_path, self.attentions[i], self.transformation_counts, prefix % (step, i), step, output_mag=False) for i in range(len(self.attentions))]
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torchvision.utils.save_image(self.lr, os.path.join(experiments_path, "attention_maps", "amap_%i_base_image.png" % (step,)))
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def get_debug_values(self, step, net_name):
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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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"grad_branch_feat_intg_std_dev": self.grad_fea_std,
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"conjoin_branch_grad_intg_std_dev": self.fea_grad_std}
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for i in range(len(means)):
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val["switch_%i_specificity" % (i,)] = means[i]
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val["switch_%i_histogram" % (i,)] = hists[i]
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return val
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@ -571,7 +571,49 @@ class QueryKeyMultiplexer(nn.Module):
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v = self.cbl2(v)
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return v.view(b, t, h, w)
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class QueryKeyPyramidMultiplexer(nn.Module):
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def __init__(self, nf, multiplexer_channels, reductions=3):
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super(QueryKeyPyramidMultiplexer, 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.reduction_blocks = nn.ModuleList([HalvingProcessingBlock(nf * 2 ** i) for i in range(reductions)])
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reduction_filters = nf * 2 ** reductions
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self.processing_blocks = nn.Sequential(OrderedDict([('block%i' % (i,), ConvGnSilu(reduction_filters, reduction_filters, kernel_size=1, norm=True, bias=False)) for i in range(3)]))
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self.expansion_blocks = nn.ModuleList([ExpansionBlock2(reduction_filters // (2 ** i)) for i in range(reductions)])
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# Blocks used to create the key
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self.key_process = ConvGnSilu(nf, nf, kernel_size=1, activation=True, norm=False, bias=True)
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# Postprocessing blocks.
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self.query_key_combine = ConvGnSilu(nf*2, nf, kernel_size=3, activation=True, norm=False, bias=False)
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self.cbl0 = ConvGnSilu(nf, nf, kernel_size=3, activation=True, norm=True, 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, transformations):
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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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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.cbl0(v)
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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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if __name__ == '__main__':
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bb = BackboneEncoder(64)
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@ -73,6 +73,11 @@ def define_G(opt, net_key='network_G', scale=None):
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netG = spsr.Spsr5(in_nc=3, out_nc=3, nf=opt_net['nf'], xforms=xforms, upscale=opt_net['scale'],
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multiplexer_reductions=opt_net['multiplexer_reductions'] if 'multiplexer_reductions' in opt_net.keys() else 2,
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init_temperature=opt_net['temperature'] if 'temperature' in opt_net.keys() else 10)
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elif which_model == "spsr6":
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xforms = opt_net['num_transforms'] if 'num_transforms' in opt_net.keys() else 8
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netG = spsr.Spsr6(in_nc=3, out_nc=3, nf=opt_net['nf'], xforms=xforms, upscale=opt_net['scale'],
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multiplexer_reductions=opt_net['multiplexer_reductions'] if 'multiplexer_reductions' in opt_net.keys() else 3,
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init_temperature=opt_net['temperature'] if 'temperature' in opt_net.keys() else 10)
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elif which_model == "ssgr1":
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xforms = opt_net['num_transforms'] if 'num_transforms' in opt_net.keys() else 8
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netG = ssg.SSGr1(in_nc=3, out_nc=3, nf=opt_net['nf'], xforms=xforms, upscale=opt_net['scale'],
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