spsr7 + cleanup
SPSR7 adds ref onto spsr6, makes more "common sense" mods.
This commit is contained in:
James Betker
parent
f9b83176f1
commit
0b5a033503
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@ -1,17 +1,20 @@
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import functools
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import os
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import math
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import torch
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import torch.nn as nn
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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, 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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from torch.utils.checkpoint import checkpoint
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from models.archs import SPSR_util as B
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from models.archs.SwitchedResidualGenerator_arch import ConfigurableSwitchComputer, ReferenceImageBranch, \
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QueryKeyMultiplexer, QueryKeyPyramidMultiplexer
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from models.archs.arch_util import ConvGnLelu, UpconvBlock, MultiConvBlock, ReferenceJoinBlock
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from switched_conv import compute_attention_specificity
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from switched_conv_util import save_attention_to_image_rgb
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from .RRDBNet_arch import RRDB
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class ImageGradient(nn.Module):
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@ -239,444 +242,6 @@ class SPSRNet(nn.Module):
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return x_out_branch, x_out, x_grad
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class SwitchedSpsr(nn.Module):
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def __init__(self, in_nc, out_nc, nf, xforms=8, upscale=4, init_temperature=10):
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super(SwitchedSpsr, 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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switch_filters = nf
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switch_reductions = 3
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switch_processing_layers = 2
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self.transformation_counts = xforms
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multiplx_fn = functools.partial(ConvBasisMultiplexer, transformation_filters, switch_filters, switch_reductions,
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switch_processing_layers, self.transformation_counts, use_exp2=True)
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pretransform_fn = functools.partial(ConvGnLelu, 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),
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transformation_filters, kernel_size=3, depth=3,
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weight_init_factor=.1)
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# Feature branch
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self.model_fea_conv = ConvGnLelu(in_nc, nf, kernel_size=3, norm=False, activation=False)
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self.sw1 = ConfigurableSwitchComputer(transformation_filters, multiplx_fn,
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pre_transform_block=pretransform_fn, 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=True)
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self.sw2 = ConfigurableSwitchComputer(transformation_filters, multiplx_fn,
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pre_transform_block=pretransform_fn, 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=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_hr_conv2 = ConvGnLelu(nf, nf, kernel_size=3, norm=False, activation=False, bias=False)
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# Grad branch
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self.get_g_nopadding = ImageGradientNoPadding()
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self.b_fea_conv = ConvGnLelu(in_nc, nf, kernel_size=3, norm=False, activation=False, bias=False)
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mplex_grad = functools.partial(ConvBasisMultiplexer, nf * 2, nf * 2, switch_reductions,
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switch_processing_layers, self.transformation_counts // 2, use_exp2=True)
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self.sw_grad = ConfigurableSwitchComputer(transformation_filters, mplex_grad,
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pre_transform_block=pretransform_fn, 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=True)
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# Upsampling
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self.grad_lr_conv = ConvGnLelu(nf, nf, kernel_size=3, norm=True, activation=True, bias=False)
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self.grad_hr_conv = ConvGnLelu(nf, nf, kernel_size=3, norm=False, activation=False, bias=False)
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# Conv used to output grad branch shortcut.
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self.grad_branch_output_conv = ConvGnLelu(nf, out_nc, kernel_size=1, norm=False, activation=False, bias=False)
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# Conjoin branch.
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# Note: "_branch_pretrain" is a special tag used to denote parameters that get pretrained before the rest.
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transform_fn_cat = functools.partial(MultiConvBlock, transformation_filters * 2, int(transformation_filters * 1.5),
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transformation_filters, kernel_size=3, depth=4,
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weight_init_factor=.1)
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pretransform_fn_cat = functools.partial(ConvGnLelu, transformation_filters * 2, transformation_filters * 2, norm=False, bias=False, weight_init_factor=.1)
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self._branch_pretrain_sw = ConfigurableSwitchComputer(transformation_filters, multiplx_fn,
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pre_transform_block=pretransform_fn_cat, transform_block=transform_fn_cat,
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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=True)
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self.upsample = nn.Sequential(*[UpconvBlock(nf, nf, block=ConvGnLelu, norm=False, activation=False, bias=False) for _ in range(n_upscale)])
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self.upsample_grad = nn.Sequential(*[UpconvBlock(nf, nf, block=ConvGnLelu, norm=False, activation=False, bias=False) for _ in range(n_upscale)])
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self.final_lr_conv = ConvGnLelu(nf, nf, kernel_size=3, norm=True, activation=False)
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self.final_hr_conv1 = ConvGnLelu(nf, nf, kernel_size=3, norm=True, activation=True, bias=False)
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self.final_hr_conv2 = ConvGnLelu(nf, out_nc, kernel_size=3, norm=False, activation=False, bias=False)
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self.switches = [self.sw1, self.sw2, self.sw_grad, self._branch_pretrain_sw]
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self.attentions = None
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self.init_temperature = init_temperature
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self.final_temperature_step = 10000
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def forward(self, x):
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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, a1 = self.sw1(x, True)
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x2, a2 = self.sw2(x1, True)
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x_fea = self.feature_lr_conv(x2)
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x_fea = self.feature_hr_conv2(x_fea)
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x_b_fea = self.b_fea_conv(x_grad)
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x_grad, a3 = self.sw_grad(x_b_fea, att_in=torch.cat([x1, x_b_fea], dim=1), output_attention_weights=True)
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x_grad = self.grad_lr_conv(x_grad)
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x_grad = self.grad_hr_conv(x_grad)
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x_out_branch = self.upsample_grad(x_grad)
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x_out_branch = self.grad_branch_output_conv(x_out_branch)
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x__branch_pretrain_cat = torch.cat([x_grad, x_fea], dim=1)
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x__branch_pretrain_cat, a4 = self._branch_pretrain_sw(x__branch_pretrain_cat, att_in=x_fea, identity=x_fea, output_attention_weights=True)
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x_out = self.final_lr_conv(x__branch_pretrain_cat)
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x_out = self.upsample(x_out)
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x_out = 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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return x_out_branch, x_out, x_grad
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def set_temperature(self, temp):
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[sw.set_temperature(temp) for sw in self.switches]
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def update_for_step(self, step, experiments_path='.'):
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if self.attentions:
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temp = max(1, 1 + self.init_temperature *
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(self.final_temperature_step - step) / self.final_temperature_step)
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self.set_temperature(temp)
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if step % 200 == 0:
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output_path = os.path.join(experiments_path, "attention_maps", "a%i")
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prefix = "attention_map_%i_%%i.png" % (step,)
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[save_attention_to_image_rgb(output_path % (i,), self.attentions[i], self.transformation_counts, prefix, step) for i in range(len(self.attentions))]
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def get_debug_values(self, step):
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temp = self.switches[0].switch.temperature
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mean_hists = [compute_attention_specificity(att, 2) for att in self.attentions]
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means = [i[0] for i in mean_hists]
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hists = [i[1].clone().detach().cpu().flatten() for i in mean_hists]
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val = {"switch_temperature": temp}
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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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class RefJoiner(nn.Module):
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def __init__(self, nf):
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super(RefJoiner, self).__init__()
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self.lin1 = nn.Linear(nf * 8, nf * 4)
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self.lin2 = nn.Linear(nf * 4, nf)
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self.join = ReferenceJoinBlock(nf, residual_weight_init_factor=.1)
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def forward(self, x, ref):
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ref = self.lin1(ref)
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ref = self.lin2(ref)
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b, _, h, w = x.shape
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ref = ref.view(b, -1, 1, 1)
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return self.join(x, ref.repeat((1, 1, h, w)))
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class ModuleWithRef(nn.Module):
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def __init__(self, nf, mcnv, *args):
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super(ModuleWithRef, self).__init__()
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self.join = ReferenceJoinBlock(nf, residual_weight_init_factor=.2)
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self.multi = mcnv(*args)
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def forward(self, x, ref):
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out, _ = self.join(x, ref)
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return self.multi(out)
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class SwitchedSpsrWithRef2(nn.Module):
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def __init__(self, in_nc, out_nc, nf, xforms=8, upscale=4, init_temperature=10):
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super(SwitchedSpsrWithRef2, 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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switch_filters = nf
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self.transformation_counts = xforms
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multiplx_fn = functools.partial(ConvBasisMultiplexer, transformation_filters, switch_filters, 3,
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2, use_exp2=True)
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pretransform_fn = functools.partial(ConvGnLelu, 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),
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transformation_filters, kernel_size=3, depth=3,
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weight_init_factor=.1)
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self.reference_processor = ReferenceImageBranch(transformation_filters)
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# Feature branch
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self.model_fea_conv = ConvGnLelu(in_nc, nf, kernel_size=3, norm=False, activation=False)
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self.noise_ref_join = ReferenceJoinBlock(nf, residual_weight_init_factor=.1)
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self.ref_join1 = RefJoiner(nf)
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self.sw1 = ConfigurableSwitchComputer(transformation_filters, multiplx_fn,
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pre_transform_block=pretransform_fn, 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)
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self.ref_join2 = RefJoiner(nf)
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self.sw2 = ConfigurableSwitchComputer(transformation_filters, multiplx_fn,
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pre_transform_block=pretransform_fn, 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)
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self.feature_lr_conv = ConvGnLelu(nf, nf, kernel_size=3, norm=True, activation=False)
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self.feature_lr_conv2 = ConvGnLelu(nf, nf, kernel_size=3, norm=False, activation=False, bias=False)
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# Grad branch. Note - groupnorm on this branch is REALLY bad. Avoid it like the plague.
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self.get_g_nopadding = ImageGradientNoPadding()
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self.grad_conv = ConvGnLelu(in_nc, nf, kernel_size=3, norm=False, activation=False, bias=False)
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self.noise_ref_join_grad = ReferenceJoinBlock(nf, residual_weight_init_factor=.1)
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self.ref_join3 = RefJoiner(nf)
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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=pretransform_fn, 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)
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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=3, 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.ref_join4 = RefJoiner(nf)
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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=pretransform_fn, 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)
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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=3, 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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def forward(self, x, ref, center_coord):
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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_stds = []
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noise_stds = []
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x_grad = self.get_g_nopadding(x)
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ref = self.reference_processor(ref, center_coord)
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x = self.model_fea_conv(x)
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x1 = x
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x1, rstd = self.ref_join1(x1, ref)
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x1, a1 = self.sw1(x1, True, identity=x)
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ref_stds.append(rstd)
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x2 = x1
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x2, nstd = self.noise_ref_join(x2, torch.randn_like(x2))
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x2, rstd = self.ref_join2(x2, ref)
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x2, a2 = self.sw2(x2, True, identity=x1)
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noise_stds.append(nstd)
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ref_stds.append(rstd)
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x_grad = self.grad_conv(x_grad)
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x_grad_identity = x_grad
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x_grad, nstd = self.noise_ref_join_grad(x_grad, torch.randn_like(x_grad))
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x_grad, rstd = self.ref_join3(x_grad, ref)
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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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noise_stds.append(nstd)
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ref_stds.append(rstd)
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x_out = x2
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x_out, nstd = self.noise_ref_join_conjoin(x_out, torch.randn_like(x_out))
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x_out, rstd = self.ref_join4(x_out, ref)
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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 = self.upsample(x_out)
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x_out = self.final_hr_conv1(x_out)
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x_out = self.final_hr_conv2(x_out)
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noise_stds.append(nstd)
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ref_stds.append(rstd)
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self.attentions = [a1, a2, a3, a4]
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self.noise_stds = torch.stack(noise_stds).mean().detach().cpu()
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self.ref_stds = torch.stack(ref_stds).mean().detach().cpu()
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self.grad_fea_std = grad_fea_std.detach().cpu()
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self.fea_grad_std = fea_grad_std.detach().cpu()
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return x_grad_out, x_out, x_grad
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def set_temperature(self, temp):
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[sw.set_temperature(temp) for sw in self.switches]
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def update_for_step(self, step, experiments_path='.'):
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if self.attentions:
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temp = max(1, 1 + self.init_temperature *
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(self.final_temperature_step - step) / self.final_temperature_step)
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self.set_temperature(temp)
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if step % 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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"reference_branch_std_dev": self.ref_stds,
|
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"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
|
||||
|
||||
|
||||
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):
|
||||
# The attention_maps debugger outputs <x>. Save that here.
|
||||
self.lr = x.detach().cpu()
|
||||
|
||||
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 % 500 == 0:
|
||||
output_path = os.path.join(experiments_path, "attention_maps")
|
||||
prefix = "amap_%i_a%i_%%i.png"
|
||||
[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))]
|
||||
torchvision.utils.save_image(self.lr, os.path.join(experiments_path, "attention_maps", "amap_%i_base_image.png" % (step,)))
|
||||
|
||||
def get_debug_values(self, step, net_name):
|
||||
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
|
||||
|
||||
|
||||
class Spsr5(nn.Module):
|
||||
def __init__(self, in_nc, out_nc, nf, xforms=8, upscale=4, multiplexer_reductions=2, init_temperature=10):
|
||||
super(Spsr5, self).__init__()
|
||||
|
|
@ -813,6 +378,8 @@ class Spsr5(nn.Module):
|
|||
return val
|
||||
|
||||
|
||||
# Variant of Spsr5 which uses multiplexer blocks that are not derived from an embedding. Also makes a few "best practices"
|
||||
# adjustments learned over the past few weeks (no noise, kernel_size=7
|
||||
class Spsr6(nn.Module):
|
||||
def __init__(self, in_nc, out_nc, nf, xforms=8, upscale=4, multiplexer_reductions=3, init_temperature=10):
|
||||
super(Spsr6, self).__init__()
|
||||
|
|
@ -935,4 +502,139 @@ class Spsr6(nn.Module):
|
|||
val["switch_%i_histogram" % (i,)] = hists[i]
|
||||
return val
|
||||
|
||||
# Variant of Spsr7 which uses multiplexer blocks that feed off of a reference embedding. Also computes that embedding.
|
||||
class Spsr7(nn.Module):
|
||||
def __init__(self, in_nc, out_nc, nf, xforms=8, upscale=4, multiplexer_reductions=3, init_temperature=10):
|
||||
super(Spsr7, self).__init__()
|
||||
n_upscale = int(math.log(upscale, 2))
|
||||
|
||||
# processing the input embedding
|
||||
self.reference_embedding = ReferenceImageBranch(nf)
|
||||
|
||||
# switch options
|
||||
self.nf = nf
|
||||
transformation_filters = nf
|
||||
self.transformation_counts = xforms
|
||||
multiplx_fn = functools.partial(QueryKeyMultiplexer, transformation_filters, embedding_channels=512, reductions=multiplexer_reductions)
|
||||
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=7, norm=False, activation=False)
|
||||
self.sw1 = ConfigurableSwitchComputer(transformation_filters, multiplx_fn,
|
||||
pre_transform_block=None, transform_block=transform_fn,
|
||||
attention_norm=True,
|
||||
transform_count=self.transformation_counts, init_temp=init_temperature,
|
||||
add_scalable_noise_to_transforms=False, feed_transforms_into_multiplexer=True)
|
||||
self.sw1_out = nn.Sequential(ConvGnLelu(nf, nf, kernel_size=3, norm=False, activation=True),
|
||||
ConvGnLelu(nf, 3, kernel_size=1, norm=False, activation=False, bias=True))
|
||||
self.sw2 = ConfigurableSwitchComputer(transformation_filters, multiplx_fn,
|
||||
pre_transform_block=None, transform_block=transform_fn,
|
||||
attention_norm=True,
|
||||
transform_count=self.transformation_counts, init_temp=init_temperature,
|
||||
add_scalable_noise_to_transforms=False, feed_transforms_into_multiplexer=True)
|
||||
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)
|
||||
self.sw2_out = nn.Sequential(ConvGnLelu(nf, nf, kernel_size=3, norm=False, activation=True),
|
||||
ConvGnLelu(nf, 3, kernel_size=1, norm=False, activation=False, bias=True))
|
||||
|
||||
# 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=7, norm=False, activation=False, bias=False)
|
||||
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=None, transform_block=transform_fn,
|
||||
attention_norm=True,
|
||||
transform_count=self.transformation_counts // 2, init_temp=init_temperature,
|
||||
add_scalable_noise_to_transforms=False, feed_transforms_into_multiplexer=True)
|
||||
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=1, 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=None, transform_block=transform_fn,
|
||||
attention_norm=True,
|
||||
transform_count=self.transformation_counts, init_temp=init_temperature,
|
||||
add_scalable_noise_to_transforms=False, feed_transforms_into_multiplexer=True)
|
||||
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=1, 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
|
||||
self.lr = None
|
||||
|
||||
def forward(self, x, ref, ref_center):
|
||||
# The attention_maps debugger outputs <x>. Save that here.
|
||||
self.lr = x.detach().cpu()
|
||||
|
||||
x_grad = self.get_g_nopadding(x)
|
||||
ref_code = self.reference_embedding(ref, ref_center)
|
||||
ref_embedding = ref_code.view(-1, self.nf * 8, 1, 1).repeat(1, 1, x.shape[2] // 8, x.shape[3] // 8)
|
||||
|
||||
x = self.model_fea_conv(x)
|
||||
x1 = x
|
||||
x1, a1 = self.sw1(x1, True, identity=x, att_in=(x1, ref_embedding))
|
||||
s1out = self.sw1_out(x1)
|
||||
|
||||
x2 = x1
|
||||
x2, a2 = self.sw2(x2, True, identity=x1, att_in=(x2, ref_embedding))
|
||||
s2out = self.sw2_out(x2)
|
||||
|
||||
x_grad = self.grad_conv(x_grad)
|
||||
x_grad_identity = 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, ref_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)
|
||||
|
||||
x_out = x2
|
||||
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, ref_embedding))
|
||||
x_out = self.final_lr_conv(x_out)
|
||||
x_out = checkpoint(self.upsample, x_out)
|
||||
x_out = checkpoint(self.final_hr_conv1, x_out)
|
||||
x_out = self.final_hr_conv2(x_out)
|
||||
|
||||
self.attentions = [a1, a2, a3, a4]
|
||||
self.grad_fea_std = grad_fea_std.detach().cpu()
|
||||
self.fea_grad_std = fea_grad_std.detach().cpu()
|
||||
return x_grad_out, x_out, s1out, s2out
|
||||
|
||||
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 % 500 == 0:
|
||||
output_path = os.path.join(experiments_path, "attention_maps")
|
||||
prefix = "amap_%i_a%i_%%i.png"
|
||||
[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))]
|
||||
torchvision.utils.save_image(self.lr, os.path.join(experiments_path, "attention_maps", "amap_%i_base_image.png" % (step,)))
|
||||
|
||||
def get_debug_values(self, step, net_name):
|
||||
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,
|
||||
"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
|
||||
|
||||
|
|
|
|||
|
|
@ -80,99 +80,6 @@ def gather_2d(input, index):
|
|||
return result
|
||||
|
||||
|
||||
# Computes a linear latent by performing processing on the reference image and returning the filters of a single point,
|
||||
# which should be centered on the image patch being processed.
|
||||
#
|
||||
# Output is base_filters * 8.
|
||||
class ReferenceImageBranch(nn.Module):
|
||||
def __init__(self, base_filters=64):
|
||||
super(ReferenceImageBranch, self).__init__()
|
||||
self.filter_conv = ConvGnSilu(4, base_filters, bias=True)
|
||||
self.reduction_blocks = nn.ModuleList([HalvingProcessingBlock(base_filters * 2 ** i) for i in range(3)])
|
||||
reduction_filters = base_filters * 2 ** 3
|
||||
self.processing_blocks = nn.Sequential(OrderedDict([('block%i' % (i,), ConvGnSilu(reduction_filters, reduction_filters, bias=False)) for i in range(4)]))
|
||||
|
||||
# center_point is a [b,2] long tensor describing the center point of where the patch was taken from the reference
|
||||
# image.
|
||||
def forward(self, x, center_point):
|
||||
x = self.filter_conv(x)
|
||||
reduction_identities = []
|
||||
for b in self.reduction_blocks:
|
||||
reduction_identities.append(x)
|
||||
x = b(x)
|
||||
x = self.processing_blocks(x)
|
||||
return gather_2d(x, center_point // 8)
|
||||
|
||||
|
||||
class AdaInConvBlock(nn.Module):
|
||||
def __init__(self, reference_size, in_nc, out_nc, conv_block=ConvGnLelu):
|
||||
super(AdaInConvBlock, self).__init__()
|
||||
self.filter_conv = conv_block(in_nc, out_nc, activation=True, norm=False, bias=False)
|
||||
self.ref_proc = nn.Linear(reference_size, reference_size)
|
||||
self.ref_red = nn.Linear(reference_size, out_nc * 2)
|
||||
self.feature_norm = torch.nn.InstanceNorm2d(out_nc)
|
||||
self.style_norm = torch.nn.InstanceNorm1d(out_nc)
|
||||
self.post_fuse_conv = conv_block(out_nc, out_nc, activation=False, norm=True, bias=True)
|
||||
|
||||
def forward(self, x, ref):
|
||||
x = self.feature_norm(self.filter_conv(x))
|
||||
ref = self.ref_proc(ref)
|
||||
ref = self.ref_red(ref)
|
||||
b, c = ref.shape
|
||||
ref = self.style_norm(ref.view(b, 2, c // 2))
|
||||
x = x * ref[:, 0, :].unsqueeze(dim=2).unsqueeze(dim=3).expand(x.shape) + ref[:, 1, :].unsqueeze(dim=2).unsqueeze(dim=3).expand(x.shape)
|
||||
return self.post_fuse_conv(x)
|
||||
|
||||
|
||||
class ProcessingBranchWithStochasticity(nn.Module):
|
||||
def __init__(self, nf_in, nf_out, noise_filters, depth):
|
||||
super(ProcessingBranchWithStochasticity, self).__init__()
|
||||
nf_gap = nf_out - nf_in
|
||||
self.noise_filters = noise_filters
|
||||
self.processor = MultiConvBlock(nf_in + noise_filters, nf_in + nf_gap // 2, nf_out, kernel_size=3, depth=depth, weight_init_factor = .1)
|
||||
|
||||
def forward(self, x):
|
||||
b, c, h, w = x.shape
|
||||
noise = torch.randn((b, self.noise_filters, h, w), device=x.device)
|
||||
return self.processor(torch.cat([x, noise], dim=1))
|
||||
|
||||
|
||||
# This is similar to ConvBasisMultiplexer, except that it takes a linear reference tensor as a second input to
|
||||
# provide better results. It also has fixed parameterization in several places
|
||||
class ReferencingConvMultiplexer(nn.Module):
|
||||
def __init__(self, input_channels, base_filters, multiplexer_channels, use_gn=True):
|
||||
super(ReferencingConvMultiplexer, self).__init__()
|
||||
self.style_fuse = AdaInConvBlock(512, input_channels, base_filters, ConvGnSilu)
|
||||
|
||||
self.reduction_blocks = nn.ModuleList([HalvingProcessingBlock(base_filters * 2 ** i) for i in range(3)])
|
||||
reduction_filters = base_filters * 2 ** 3
|
||||
self.processing_blocks = nn.Sequential(OrderedDict([('block%i' % (i,), ConvGnSilu(reduction_filters, reduction_filters, bias=False)) for i in range(2)]))
|
||||
self.expansion_blocks = nn.ModuleList([ExpansionBlock2(reduction_filters // (2 ** i)) for i in range(3)])
|
||||
|
||||
gap = base_filters - multiplexer_channels
|
||||
cbl1_out = ((base_filters - (gap // 2)) // 4) * 4 # Must be multiples of 4 to use with group norm.
|
||||
self.cbl1 = ConvGnSilu(base_filters, cbl1_out, norm=use_gn, bias=False, num_groups=4)
|
||||
cbl2_out = ((base_filters - (3 * gap // 4)) // 4) * 4
|
||||
self.cbl2 = ConvGnSilu(cbl1_out, cbl2_out, norm=use_gn, bias=False, num_groups=4)
|
||||
self.cbl3 = ConvGnSilu(cbl2_out, multiplexer_channels, bias=True, norm=False)
|
||||
|
||||
def forward(self, x, ref):
|
||||
x = self.style_fuse(x, ref)
|
||||
|
||||
reduction_identities = []
|
||||
for b in self.reduction_blocks:
|
||||
reduction_identities.append(x)
|
||||
x = b(x)
|
||||
x = self.processing_blocks(x)
|
||||
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 ConfigurableSwitchComputer(nn.Module):
|
||||
def __init__(self, base_filters, multiplexer_net, pre_transform_block, transform_block, transform_count, attention_norm,
|
||||
init_temp=20, add_scalable_noise_to_transforms=False, feed_transforms_into_multiplexer=False):
|
||||
|
|
@ -476,9 +383,28 @@ class BackboneResnet(nn.Module):
|
|||
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.
|
||||
# Computes a linear latent by performing processing on the reference image and returning the filters of a single point,
|
||||
# which should be centered on the image patch being processed.
|
||||
#
|
||||
# Output is base_filters * 8.
|
||||
class ReferenceImageBranch(nn.Module):
|
||||
def __init__(self, base_filters=64):
|
||||
super(ReferenceImageBranch, self).__init__()
|
||||
self.features = nn.Sequential(ConvGnSilu(4, base_filters, kernel_size=7, bias=True),
|
||||
HalvingProcessingBlock(base_filters),
|
||||
ConvGnSilu(base_filters*2, base_filters*2, activation=True, norm=True, bias=False),
|
||||
HalvingProcessingBlock(base_filters*2),
|
||||
ConvGnSilu(base_filters*4, base_filters*4, activation=True, norm=True, bias=False),
|
||||
HalvingProcessingBlock(base_filters*4),
|
||||
ConvGnSilu(base_filters*8, base_filters*8, activation=True, norm=True, bias=False),
|
||||
ConvGnSilu(base_filters*8, base_filters*8, activation=True, norm=True, bias=False))
|
||||
|
||||
# center_point is a [b,2] long tensor describing the center point of where the patch was taken from the reference
|
||||
# image.
|
||||
def forward(self, x, center_point):
|
||||
x = self.features(x)
|
||||
return gather_2d(x, center_point // 8) # Divide by 8 to scale the center_point down.
|
||||
|
||||
|
||||
# Mutiplexer that combines a structured embedding with a contextual switch input to guide alterations to that input.
|
||||
#
|
||||
|
|
@ -526,12 +452,12 @@ class EmbeddingMultiplexer(nn.Module):
|
|||
|
||||
|
||||
class QueryKeyMultiplexer(nn.Module):
|
||||
def __init__(self, nf, multiplexer_channels, reductions=2):
|
||||
def __init__(self, nf, multiplexer_channels, embedding_channels=256, 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.embedding_process = ConvGnSilu(embedding_channels, 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(
|
||||
|
|
@ -615,6 +541,7 @@ class QueryKeyPyramidMultiplexer(nn.Module):
|
|||
|
||||
return v.view(b, t, h, w)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
bb = BackboneEncoder(64)
|
||||
emb = QueryKeyMultiplexer(64, 10)
|
||||
|
|
|
|||
|
|
@ -56,18 +56,6 @@ def define_G(opt, net_key='network_G', scale=None):
|
|||
elif which_model == 'spsr_net_improved':
|
||||
netG = spsr.SPSRNetSimplified(in_nc=opt_net['in_nc'], out_nc=opt_net['out_nc'], nf=opt_net['nf'],
|
||||
nb=opt_net['nb'], upscale=opt_net['scale'])
|
||||
elif which_model == "spsr_switched":
|
||||
xforms = opt_net['num_transforms'] if 'num_transforms' in opt_net.keys() else 8
|
||||
netG = spsr.SwitchedSpsr(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 == "spsr_switched_with_ref2" or which_model == "spsr3":
|
||||
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 == "spsr5":
|
||||
xforms = opt_net['num_transforms'] if 'num_transforms' in opt_net.keys() else 8
|
||||
netG = spsr.Spsr5(in_nc=3, out_nc=3, nf=opt_net['nf'], xforms=xforms, upscale=opt_net['scale'],
|
||||
|
|
@ -78,6 +66,11 @@ def define_G(opt, net_key='network_G', scale=None):
|
|||
netG = spsr.Spsr6(in_nc=3, out_nc=3, nf=opt_net['nf'], xforms=xforms, upscale=opt_net['scale'],
|
||||
multiplexer_reductions=opt_net['multiplexer_reductions'] if 'multiplexer_reductions' in opt_net.keys() else 3,
|
||||
init_temperature=opt_net['temperature'] if 'temperature' in opt_net.keys() else 10)
|
||||
elif which_model == "spsr7":
|
||||
xforms = opt_net['num_transforms'] if 'num_transforms' in opt_net.keys() else 8
|
||||
netG = spsr.Spsr7(in_nc=3, out_nc=3, nf=opt_net['nf'], xforms=xforms, upscale=opt_net['scale'],
|
||||
multiplexer_reductions=opt_net['multiplexer_reductions'] if 'multiplexer_reductions' in opt_net.keys() else 3,
|
||||
init_temperature=opt_net['temperature'] if 'temperature' in opt_net.keys() else 10)
|
||||
elif which_model == "ssgr1":
|
||||
xforms = opt_net['num_transforms'] if 'num_transforms' in opt_net.keys() else 8
|
||||
netG = ssg.SSGr1(in_nc=3, out_nc=3, nf=opt_net['nf'], xforms=xforms, upscale=opt_net['scale'],
|
||||
|
|
|
|||
|
|
@ -32,7 +32,7 @@ def init_dist(backend='nccl', **kwargs):
|
|||
def main():
|
||||
#### options
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/train_exd_imgset_spsr5_spine_no_pretrain.yml')
|
||||
parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/train_exd_imgset_spsr7_multiloss.yml')
|
||||
parser.add_argument('--launcher', choices=['none', 'pytorch'], default='none', help='job launcher')
|
||||
parser.add_argument('--local_rank', type=int, default=0)
|
||||
args = parser.parse_args()
|
||||
|
|
|
|||
Loading…
Reference in New Issue
Block a user