Swap recurrence
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6141aa1110
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@ -3,7 +3,7 @@ from torch import nn
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from models.archs.SPSR_arch import ImageGradientNoPadding
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from models.archs.arch_util import ConvGnLelu, ExpansionBlock2, ConvGnSilu, ConjoinBlock, MultiConvBlock, \
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FinalUpsampleBlock2x
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FinalUpsampleBlock2x, ReferenceJoinBlock
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from models.archs.spinenet_arch import SpineNet
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from utils.util import checkpoint
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@ -69,10 +69,10 @@ class ChainedEmbeddingGenWithStructure(nn.Module):
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def __init__(self, depth=10, recurrent=False):
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super(ChainedEmbeddingGenWithStructure, self).__init__()
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self.recurrent = recurrent
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self.initial_conv = ConvGnLelu(3, 64, kernel_size=7, bias=True, norm=False, activation=False)
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if recurrent:
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self.initial_conv_rec = ConvGnLelu(6, 64, kernel_size=7, bias=True, norm=False, activation=False)
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else:
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self.initial_conv = ConvGnLelu(3, 64, kernel_size=7, bias=True, norm=False, activation=False)
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self.recurrent_process = ConvGnLelu(3, 64, kernel_size=3, stride=2, norm=False, bias=True, activation=False)
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self.recurrent_join = ReferenceJoinBlock(64, residual_weight_init_factor=.01, final_norm=False, kernel_size=1, depth=3, join=False)
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self.spine = SpineNet(arch='49', output_level=[3, 4], double_reduce_early=False)
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self.blocks = nn.ModuleList([BasicEmbeddingPyramid() for i in range(depth)])
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self.structure_joins = nn.ModuleList([ConjoinBlock(64) for i in range(3)])
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@ -83,11 +83,10 @@ class ChainedEmbeddingGenWithStructure(nn.Module):
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def forward(self, x, recurrent=None):
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emb = checkpoint(self.spine, x)
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fea = self.initial_conv(x)
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if self.recurrent:
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fea = torch.cat([x,recurrent], dim=1)
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fea = self.initial_conv_rec(x)
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else:
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fea = self.initial_conv(x)
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rec = self.recurrent_process(recurrent)
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fea, _ = self.recurrent_join(fea, rec)
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grad = fea
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for i, block in enumerate(self.blocks):
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fea = fea + checkpoint(block, fea, *emb)
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@ -1,24 +1,24 @@
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import functools
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import logging
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from collections import OrderedDict
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import munch
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import torch
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import logging
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import torchvision
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from munch import munchify
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import models.archs.SRResNet_arch as SRResNet_arch
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import models.archs.discriminator_vgg_arch as SRGAN_arch
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import models.archs.DiscriminatorResnet_arch as DiscriminatorResnet_arch
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import models.archs.DiscriminatorResnet_arch_passthrough as DiscriminatorResnet_arch_passthrough
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import models.archs.RRDBNet_arch as RRDBNet_arch
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import models.archs.feature_arch as feature_arch
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import models.archs.SwitchedResidualGenerator_arch as SwitchedGen_arch
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import models.archs.SPSR_arch as spsr
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import models.archs.SRResNet_arch as SRResNet_arch
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import models.archs.StructuredSwitchedGenerator as ssg
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import models.archs.rcan as rcan
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import models.archs.SwitchedResidualGenerator_arch as SwitchedGen_arch
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import models.archs.discriminator_vgg_arch as SRGAN_arch
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import models.archs.feature_arch as feature_arch
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import models.archs.panet.panet as panet
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from collections import OrderedDict
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import torchvision
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import functools
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from models.archs.ChainedEmbeddingGen import ChainedEmbeddingGen, ChainedEmbeddingGenWithStructure, \
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ChainedEmbeddingGenWithStructureR2
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import models.archs.rcan as rcan
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from models.archs.ChainedEmbeddingGen import ChainedEmbeddingGen, ChainedEmbeddingGenWithStructure
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logger = logging.getLogger('base')
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