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Remove RRDB with switching

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This idea never really panned out, removing it.
This commit is contained in:
James Betker 2020-07-01 12:08:32 -06:00
parent e2398ac83c
commit 480d1299d7
2 changed files with 4 additions and 173 deletions
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163
codes/models/archs/RRDBNet_arch.py
View File

@ -5,7 +5,6 @@ import torch.nn.functional as F
import models.archs.arch_util as arch_util import models.archs.arch_util as arch_util
from models.archs.arch_util import PixelUnshuffle from models.archs.arch_util import PixelUnshuffle
import torchvision import torchvision
import switched_conv as switched_conv
class ResidualDenseBlock_5C(nn.Module): class ResidualDenseBlock_5C(nn.Module):
@ -32,91 +31,6 @@ class ResidualDenseBlock_5C(nn.Module):
return x5 * 0.2 + x return x5 * 0.2 + x
# Multiple 5-channel residual block that uses learned switching to diversify its outputs.
# If multi_head_input=False: takes standard (b,f,w,h) input tensor; else takes (b,heads,f,w,h) input tensor. Note that the default RDB block does not support this format, so use SwitchedRDB_5C_MultiHead for this case.
# If collapse_heads=True, outputs (b,f,w,h) tensor.
# If collapse_heads=False, outputs (b,heads,f,w,h) tensor.
class SwitchedRDB_5C(switched_conv.MultiHeadSwitchedAbstractBlock):
def __init__(self, nf=64, gc=32, num_convs=8, num_heads=2, include_skip_head=False, init_temperature=1, multi_head_input=False, collapse_heads=True, force_block=None):
if force_block is None:
rdb5c = functools.partial(ResidualDenseBlock_5C, nf, gc)
else:
rdb5c = force_block
super(SwitchedRDB_5C, self).__init__(
rdb5c,
nf,
num_convs,
num_heads,
att_kernel_size=3,
att_pads=1,
include_skip_head=include_skip_head,
initial_temperature=init_temperature,
multi_head_input=multi_head_input,
concat_heads_into_filters=collapse_heads,
)
self.collapse_heads = collapse_heads
if self.collapse_heads:
self.mhead_collapse = nn.Conv2d(num_heads * nf, nf, 1)
arch_util.initialize_weights([self.mhead_collapse], 1)
arch_util.initialize_weights([sw.attention_conv1 for sw in self.switches] +
[sw.attention_conv2 for sw in self.switches], 1)
def forward(self, x, output_attention_weights=False):
outs = super(SwitchedRDB_5C, self).forward(x, output_attention_weights)
if output_attention_weights:
outs, atts = outs
if self.collapse_heads:
# outs need to be collapsed back down to a single heads worth of data.
out = self.mhead_collapse(outs)
else:
out = outs
return out, atts
# Implementation of ResidualDenseBlock_5C which compresses multiple switching heads via a Conv3d before doing RDB
# computation.
class ResidualDenseBlock_5C_WithMheadConverter(ResidualDenseBlock_5C):
def __init__(self, nf=64, gc=32, bias=True, heads=2):
# Switched blocks generally operate at low resolution, kernel size is much less important, therefore set to 1.
super(ResidualDenseBlock_5C_WithMheadConverter, self).__init__(nf=nf, gc=gc, bias=bias, late_stage_kernel_size=1,
late_stage_padding=0)
self.heads = heads
self.converter = nn.Conv3d(nf, nf, kernel_size=(heads, 1, 1), stride=(heads, 1, 1))
arch_util.initialize_weights(self.converter)
# Accepts input of shape (b, heads, f, w, h)
def forward(self, x):
# Permute filter dim to 1.
x = x.permute(0, 2, 1, 3, 4)
x = self.converter(x)
x = torch.squeeze(x, dim=2)
return super(ResidualDenseBlock_5C_WithMheadConverter, self).forward(x)
# Multiple 5-channel residual block that uses learned switching to diversify its outputs. The difference between this
# block and SwitchedRDB_5C is this block accepts multi-headed inputs of format (b,heads,f,w,h).
#
# It does this by performing a Conv3d on the first block, which convolves all heads and collapses them to a dimension
# of 1. The tensor is then squeezed and performs identically to SwitchedRDB_5C from there.
class SwitchedRDB_5C_MultiHead(SwitchedRDB_5C):
def __init__(self, nf=64, gc=32, num_convs=8, num_heads=2, include_skip_head=False, init_temperature=1, collapse_heads=False):
rdb5c = functools.partial(ResidualDenseBlock_5C_WithMheadConverter, nf, gc, heads=num_heads)
super(SwitchedRDB_5C_MultiHead, self).__init__(
nf=nf,
gc=gc,
num_convs=num_convs,
num_heads=num_heads,
include_skip_head=include_skip_head,
init_temperature=init_temperature,
multi_head_input=True,
collapse_heads=collapse_heads,
force_block=rdb5c,
)
class RRDB(nn.Module): class RRDB(nn.Module):
'''Residual in Residual Dense Block''' '''Residual in Residual Dense Block'''
@ -145,49 +59,6 @@ class LowDimRRDB(RRDB):
return self.shuffle(x) return self.shuffle(x)
# RRDB block that uses switching on the individual RDB modules that compose it to increase learning diversity.
class SwitchedRRDB(RRDB):
def __init__(self, nf, gc=32, num_convs=8, init_temperature=1, final_temperature_step=1, switching_block=SwitchedRDB_5C):
super(SwitchedRRDB, self).__init__(nf, gc)
self.RDB1 = switching_block(nf, gc, num_convs=num_convs, init_temperature=init_temperature)
self.RDB2 = switching_block(nf, gc, num_convs=num_convs, init_temperature=init_temperature)
self.RDB3 = switching_block(nf, gc, num_convs=num_convs, init_temperature=init_temperature)
self.init_temperature = init_temperature
self.final_temperature_step = final_temperature_step
self.running_mean = 0
self.running_count = 0
def set_temperature(self, temp):
[sw.set_attention_temperature(temp) for sw in self.RDB1.switches]
[sw.set_attention_temperature(temp) for sw in self.RDB2.switches]
[sw.set_attention_temperature(temp) for sw in self.RDB3.switches]
def forward(self, x):
out, att1 = self.RDB1(x, True)
out, att2 = self.RDB2(out, True)
out, att3 = self.RDB3(out, True)
a1mean, _ = switched_conv.compute_attention_specificity(att1, 2)
a2mean, _ = switched_conv.compute_attention_specificity(att2, 2)
a3mean, _ = switched_conv.compute_attention_specificity(att3, 2)
self.running_mean += (a1mean + a2mean + a3mean) / 3.0
self.running_count += 1
return out * 0.2 + x
def get_debug_values(self, step, prefix):
# Take the chance to update the temperature here.
temp = max(1, int(self.init_temperature * (self.final_temperature_step - step) / self.final_temperature_step))
self.set_temperature(temp)
# Intentionally overwrite attention_temperature from other RRDB blocks; these should be synced.
val = {"%s_attention_mean" % (prefix,): self.running_mean / self.running_count,
"attention_temperature": temp}
self.running_count = 0
self.running_mean = 0
return val
# Identical to LowDimRRDB but wraps an RRDB rather than inheriting from it. TODO: remove LowDimRRDB when backwards # Identical to LowDimRRDB but wraps an RRDB rather than inheriting from it. TODO: remove LowDimRRDB when backwards
# compatibility is no longer desired. # compatibility is no longer desired.
class LowDimRRDBWrapper(nn.Module): class LowDimRRDBWrapper(nn.Module):
@ -203,36 +74,6 @@ class LowDimRRDBWrapper(nn.Module):
x = self.rrdb(x) x = self.rrdb(x)
return self.shuffle(x) return self.shuffle(x)
# RRDB block that uses multi-headed switching on multiple individual RDB blocks to improve diversity. Multiple heads
# are annealed internally. This variant has a depth of 4 RDB blocks, rather than 3 like others above.
class SwitchedMultiHeadRRDB(SwitchedRRDB):
def __init__(self, nf, gc=32, num_convs=8, num_heads=2, init_temperature=1, final_temperature_step=1):
super(SwitchedMultiHeadRRDB, self).__init__(nf=nf, gc=gc, num_convs=num_convs, init_temperature=init_temperature, final_temperature_step=final_temperature_step)
self.RDB1 = SwitchedRDB_5C(nf, gc, num_convs=num_convs, num_heads=num_heads, include_skip_head=True, init_temperature=init_temperature, collapse_heads=False)
self.RDB2 = SwitchedRDB_5C_MultiHead(nf, gc, num_convs=num_convs, num_heads=num_heads, include_skip_head=True, init_temperature=init_temperature, collapse_heads=False)
self.RDB3 = SwitchedRDB_5C_MultiHead(nf, gc, num_convs=num_convs, num_heads=num_heads, include_skip_head=True, init_temperature=init_temperature, collapse_heads=False)
self.RDB4 = SwitchedRDB_5C_MultiHead(nf, gc, num_convs=num_convs, num_heads=num_heads, include_skip_head=True, init_temperature=init_temperature, collapse_heads=True)
def set_temperature(self, temp):
[sw.set_attention_temperature(temp) for sw in self.RDB1.switches]
[sw.set_attention_temperature(temp) for sw in self.RDB2.switches]
[sw.set_attention_temperature(temp) for sw in self.RDB3.switches]
[sw.set_attention_temperature(temp) for sw in self.RDB4.switches]
def forward(self, x):
out, att1 = self.RDB1(x, True)
out, att2 = self.RDB2(out, True)
out, att3 = self.RDB3(out, True)
out, att4 = self.RDB4(out, True)
a1mean, _ = switched_conv.compute_attention_specificity(att1, 2)
a2mean, _ = switched_conv.compute_attention_specificity(att2, 2)
a3mean, _ = switched_conv.compute_attention_specificity(att3, 2)
a4mean, _ = switched_conv.compute_attention_specificity(att4, 2)
self.running_mean += (a1mean + a2mean + a3mean + a4mean) / 3.0
self.running_count += 1
return out * 0.2 + x
# This module performs the majority of the processing done by RRDBNet. It just doesn't have the upsampling at the end. # This module performs the majority of the processing done by RRDBNet. It just doesn't have the upsampling at the end.
class RRDBTrunk(nn.Module): class RRDBTrunk(nn.Module):
@ -270,6 +111,7 @@ class RRDBTrunk(nn.Module):
i += 1 i += 1
return val return val
# Adds some base methods that all RRDB* classes will use. # Adds some base methods that all RRDB* classes will use.
class RRDBBase(nn.Module): class RRDBBase(nn.Module):
def __init__(self): def __init__(self):
@ -331,6 +173,7 @@ class RRDBNet(RRDBBase):
state_dict["trunk.%s" % (k,)] = state_dict.pop(k) state_dict["trunk.%s" % (k,)] = state_dict.pop(k)
super(RRDBNet, self).load_state_dict(state_dict, strict) super(RRDBNet, self).load_state_dict(state_dict, strict)
# Variant of RRDBNet that is "assisted" by an external pretrained image classifier whose # Variant of RRDBNet that is "assisted" by an external pretrained image classifier whose
# intermediate layers have been splayed out, pixel-shuffled, and fed back in. # intermediate layers have been splayed out, pixel-shuffled, and fed back in.
# TODO: Convert to use new RRDBBase hierarchy. # TODO: Convert to use new RRDBBase hierarchy.
@ -413,6 +256,7 @@ class AssistedRRDBNet(nn.Module):
return (out,) return (out,)
class PixShuffleInitialConv(nn.Module): class PixShuffleInitialConv(nn.Module):
def __init__(self, reduction_factor, nf_out): def __init__(self, reduction_factor, nf_out):
super(PixShuffleInitialConv, self).__init__() super(PixShuffleInitialConv, self).__init__()
@ -427,6 +271,7 @@ class PixShuffleInitialConv(nn.Module):
x = self.unshuffle(x) x = self.unshuffle(x)
return self.conv(x) return self.conv(x)
# This class uses a RRDBTrunk to perform processing on an image, then upsamples it. # This class uses a RRDBTrunk to perform processing on an image, then upsamples it.
class PixShuffleRRDB(RRDBBase): class PixShuffleRRDB(RRDBBase):
def __init__(self, nf, nb, gc=32, scale=2, rrdb_block_f=None): def __init__(self, nf, nb, gc=32, scale=2, rrdb_block_f=None):

14
codes/models/networks.py
View File

@ -32,25 +32,11 @@ def define_G(opt, net_key='network_G'):
elif which_model == 'AssistedRRDBNet': elif which_model == 'AssistedRRDBNet':
netG = RRDBNet_arch.AssistedRRDBNet(in_nc=opt_net['in_nc'], out_nc=opt_net['out_nc'], netG = RRDBNet_arch.AssistedRRDBNet(in_nc=opt_net['in_nc'], out_nc=opt_net['out_nc'],
nf=opt_net['nf'], nb=opt_net['nb'], scale=scale) nf=opt_net['nf'], nb=opt_net['nb'], scale=scale)
elif which_model == 'AttentiveRRDBNet':
netG = RRDBNet_arch.RRDBNet(in_nc=opt_net['in_nc'], out_nc=opt_net['out_nc'],
nf=opt_net['nf'], nb=opt_net['nb'], scale=scale,
rrdb_block_f=functools.partial(RRDBNet_arch.SwitchedRRDB, nf=opt_net['nf'], gc=opt_net['gc'],
init_temperature=opt_net['temperature'],
final_temperature_step=opt_net['temperature_final_step']))
elif which_model == 'LowDimRRDBNet': elif which_model == 'LowDimRRDBNet':
gen_scale = scale * opt_net['initial_stride'] gen_scale = scale * opt_net['initial_stride']
rrdb = functools.partial(RRDBNet_arch.LowDimRRDB, nf=opt_net['nf'], gc=opt_net['gc'], dimensional_adjustment=opt_net['dim']) rrdb = functools.partial(RRDBNet_arch.LowDimRRDB, nf=opt_net['nf'], gc=opt_net['gc'], dimensional_adjustment=opt_net['dim'])
netG = RRDBNet_arch.RRDBNet(in_nc=opt_net['in_nc'], out_nc=opt_net['out_nc'], netG = RRDBNet_arch.RRDBNet(in_nc=opt_net['in_nc'], out_nc=opt_net['out_nc'],
nf=opt_net['nf'], nb=opt_net['nb'], scale=gen_scale, rrdb_block_f=rrdb, initial_stride=opt_net['initial_stride']) nf=opt_net['nf'], nb=opt_net['nb'], scale=gen_scale, rrdb_block_f=rrdb, initial_stride=opt_net['initial_stride'])
elif which_model == "LowDimRRDBWithMultiHeadSwitching":
gen_scale = scale * opt_net['initial_stride']
switcher = functools.partial(RRDBNet_arch.SwitchedMultiHeadRRDB, num_convs=opt_net['num_convs'], num_heads=opt_net['num_heads'],
init_temperature=opt_net['temperature'], final_temperature_step=opt_net['temperature_final_step'])
rrdb = functools.partial(RRDBNet_arch.LowDimRRDBWrapper, nf=opt_net['nf'], gc=opt_net['gc'], dimensional_adjustment=opt_net['dim'],
partial_rrdb=switcher)
netG = RRDBNet_arch.RRDBNet(in_nc=opt_net['in_nc'], out_nc=opt_net['out_nc'],
nf=opt_net['nf'], nb=opt_net['nb'], scale=gen_scale, rrdb_block_f=rrdb, initial_stride=opt_net['initial_stride'])
elif which_model == 'PixRRDBNet': elif which_model == 'PixRRDBNet':
block_f = None block_f = None
if opt_net['attention']: if opt_net['attention']: