DL-Art-School/codes/models/archs/RRDBNet_arch.py
James Betker be7982b9ae Add skip heads to switcher
These pass through the input so that it can be selected by the attention mechanism.
2020-06-14 12:46:54 -06:00

463 lines
20 KiB
Python

import functools
import torch
import torch.nn as nn
import torch.nn.functional as F
import models.archs.arch_util as arch_util
from models.archs.arch_util import PixelUnshuffle
import torchvision
import switched_conv as switched_conv
class ResidualDenseBlock_5C(nn.Module):
def __init__(self, nf=64, gc=32, bias=True, late_stage_kernel_size=3, late_stage_padding=1):
super(ResidualDenseBlock_5C, self).__init__()
# gc: growth channel, i.e. intermediate channels
self.conv1 = nn.Conv2d(nf, gc, 3, 1, 1, bias=bias)
self.conv2 = nn.Conv2d(nf + gc, gc, 3, 1, 1, bias=bias)
self.conv3 = nn.Conv2d(nf + 2 * gc, gc, late_stage_kernel_size, 1, late_stage_padding, bias=bias)
self.conv4 = nn.Conv2d(nf + 3 * gc, gc, late_stage_kernel_size, 1, late_stage_padding, bias=bias)
self.conv5 = nn.Conv2d(nf + 4 * gc, nf, late_stage_kernel_size, 1, late_stage_padding, bias=bias)
self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
# initialization
arch_util.initialize_weights([self.conv1, self.conv2, self.conv3, self.conv4, self.conv5],
0.1)
def forward(self, x):
x1 = self.lrelu(self.conv1(x))
x2 = self.lrelu(self.conv2(torch.cat((x, x1), 1)))
x3 = self.lrelu(self.conv3(torch.cat((x, x1, x2), 1)))
x4 = self.lrelu(self.conv4(torch.cat((x, x1, x2, x3), 1)))
x5 = self.conv5(torch.cat((x, x1, x2, x3, x4), 1))
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))
# 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):
'''Residual in Residual Dense Block'''
def __init__(self, nf, gc=32):
super(RRDB, self).__init__()
self.RDB1 = ResidualDenseBlock_5C(nf, gc)
self.RDB2 = ResidualDenseBlock_5C(nf, gc)
self.RDB3 = ResidualDenseBlock_5C(nf, gc)
def forward(self, x):
out = self.RDB1(x)
out = self.RDB2(out)
out = self.RDB3(out)
return out * 0.2 + x
class LowDimRRDB(RRDB):
def __init__(self, nf, gc=32, dimensional_adjustment=4):
super(LowDimRRDB, self).__init__(nf * (dimensional_adjustment ** 2), gc * (dimensional_adjustment ** 2))
self.unshuffle = PixelUnshuffle(dimensional_adjustment)
self.shuffle = nn.PixelShuffle(dimensional_adjustment)
def forward(self, x):
x = self.unshuffle(x)
x = super(LowDimRRDB, self).forward(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
# compatibility is no longer desired.
class LowDimRRDBWrapper(nn.Module):
# Do not specify nf or gc on the partial_rrdb passed in. That will be done by the wrapper.
def __init__(self, nf, partial_rrdb, gc=32, dimensional_adjustment=4):
super(LowDimRRDBWrapper, self).__init__()
self.rrdb = partial_rrdb(nf=nf * (dimensional_adjustment ** 2), gc=gc * (dimensional_adjustment ** 2))
self.unshuffle = PixelUnshuffle(dimensional_adjustment)
self.shuffle = nn.PixelShuffle(dimensional_adjustment)
def forward(self, x):
x = self.unshuffle(x)
x = self.rrdb(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.
class RRDBTrunk(nn.Module):
def __init__(self, nf_in, nf_out, nb, gc=32, initial_stride=1, rrdb_block_f=None, conv_first_block=None):
super(RRDBTrunk, self).__init__()
if rrdb_block_f is None:
rrdb_block_f = functools.partial(RRDB, nf=nf_out, gc=gc)
if conv_first_block is None:
self.conv_first = nn.Conv2d(nf_in, nf_out, 7, initial_stride, padding=3, bias=True)
else:
self.conv_first = conv_first_block
self.RRDB_trunk, self.rrdb_layers = arch_util.make_layer(rrdb_block_f, nb, True)
self.trunk_conv = nn.Conv2d(nf_out, nf_out, 3, 1, 1, bias=True)
# Sets the softmax temperature of each RRDB layer. Only works if you are using attentive
# convolutions.
def set_temperature(self, temp):
for layer in self.rrdb_layers:
layer.set_temperature(temp)
def forward(self, x):
fea = self.conv_first(x)
trunk = self.trunk_conv(self.RRDB_trunk(fea))
fea = fea + trunk
return fea
def get_debug_values(self, step, prefix):
val = {}
i = 0
for block in self.RRDB_trunk._modules.values():
if hasattr(block, "get_debug_values"):
val.update(block.get_debug_values(step, "%s_rdb_%i" % (prefix, i)))
i += 1
return val
# Adds some base methods that all RRDB* classes will use.
class RRDBBase(nn.Module):
def __init__(self):
super(RRDBBase, self).__init__()
# Sets the softmax temperature of each RRDB layer. Only works if you are using attentive
# convolutions.
def set_temperature(self, temp):
for trunk in self.trunks:
for layer in trunk.rrdb_layers:
layer.set_temperature(temp)
def get_debug_values(self, step):
val = {}
for i, trunk in enumerate(self.trunks):
for j, block in enumerate(trunk.RRDB_trunk._modules.values()):
if hasattr(block, "get_debug_values"):
val.update(block.get_debug_values(step, "trunk_%i_block_%i" % (i, j)))
return val
# This class uses a RRDBTrunk to perform processing on an image, then upsamples it.
class RRDBNet(RRDBBase):
def __init__(self, in_nc, out_nc, nf, nb, gc=32, scale=2, initial_stride=1,
rrdb_block_f=None):
super(RRDBNet, self).__init__()
# Trunk - does actual processing.
self.trunk = RRDBTrunk(in_nc, nf, nb, gc, initial_stride, rrdb_block_f)
self.trunks = [self.trunk]
# Upsampling
self.scale = scale
self.upconv1 = nn.Conv2d(nf, nf, 5, 1, padding=2, bias=True)
self.upconv2 = nn.Conv2d(nf, nf, 5, 1, padding=2, bias=True)
self.HRconv = nn.Conv2d(nf, nf, 5, 1, padding=2, bias=True)
self.conv_last = nn.Conv2d(nf, out_nc, 3, 1, 1, bias=True)
self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
def forward(self, x):
fea = self.trunk(x)
if self.scale >= 2:
fea = F.interpolate(fea, scale_factor=2, mode='nearest')
fea = self.lrelu(self.upconv1(fea))
if self.scale >= 4:
fea = F.interpolate(fea, scale_factor=2, mode='nearest')
fea = self.lrelu(self.upconv2(fea))
out = self.conv_last(self.lrelu(self.HRconv(fea)))
return (out,)
def load_state_dict(self, state_dict, strict=True):
# The parameters in self.trunk used to be in this class. To support loading legacy saves, restore them.
t_state = self.trunk.state_dict()
for k in t_state.keys():
if k in state_dict.keys():
state_dict["trunk.%s" % (k,)] = state_dict.pop(k)
super(RRDBNet, self).load_state_dict(state_dict, strict)
# 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.
# TODO: Convert to use new RRDBBase hierarchy.
class AssistedRRDBNet(nn.Module):
# in_nc=number of input channels.
# out_nc=number of output channels.
# nf=internal filter count
# nb=number of additional blocks after the assistance layers.
# gc=growth channel inside of residual blocks
# scale=the number of times the output is doubled in size.
# initial_stride=the stride on the first conv. can be used to downsample the image for processing.
def __init__(self, in_nc, out_nc, nf, nb, gc=32, scale=2, initial_stride=1):
super(AssistedRRDBNet, self).__init__()
self.scale = scale
self.conv_first = nn.Conv2d(in_nc, nf, 7, initial_stride, padding=3, bias=True)
# Set-up the assist-net, which should do feature extraction for us.
self.assistnet = torchvision.models.wide_resnet50_2(pretrained=True)
self.set_enable_assistnet_training(False)
assist_nf = [4, 8, 16] # Fixed for resnet. Re-evaluate if using other networks.
self.assist2 = RRDB(nf + assist_nf[0], gc)
self.assist3 = RRDB(nf + sum(assist_nf[:2]), gc)
self.assist4 = RRDB(nf + sum(assist_nf), gc)
nf = nf + sum(assist_nf)
# After this, it's just a "standard" RRDB net.
RRDB_block_f = functools.partial(RRDB, nf=nf, gc=gc)
self.RRDB_trunk = arch_util.make_layer(RRDB_block_f, nb)
self.trunk_conv = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
#### upsampling
self.upconv1 = nn.Conv2d(nf, nf, 5, 1, padding=2, bias=True)
self.upconv2 = nn.Conv2d(nf, nf, 5, 1, padding=2, bias=True)
self.HRconv = nn.Conv2d(nf, nf, 5, 1, padding=2, bias=True)
self.conv_last = nn.Conv2d(nf, out_nc, 3, 1, 1, bias=True)
self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
def set_enable_assistnet_training(self, en):
for p in self.assistnet.parameters():
p.requires_grad = en
def res_extract(self, x):
# Width and height must be factors of 16 to use this architecture. Check that here.
(b, f, w, h) = x.shape
assert w % 16 == 0
assert h % 16 == 0
x = self.assistnet.conv1(x)
x = self.assistnet.bn1(x)
x = self.assistnet.relu(x)
x = self.assistnet.maxpool(x)
x = self.assistnet.layer1(x)
l1 = F.pixel_shuffle(x, 4)
x = self.assistnet.layer2(x)
l2 = F.pixel_shuffle(x, 8)
x = self.assistnet.layer3(x)
l3 = F.pixel_shuffle(x, 16)
return l1, l2, l3
def forward(self, x):
# Invoke the assistant net first.
l1, l2, l3 = self.res_extract(x)
fea = self.conv_first(x)
fea = self.assist2(torch.cat([fea, l3], dim=1))
fea = self.assist3(torch.cat([fea, l2], dim=1))
fea = self.assist4(torch.cat([fea, l1], dim=1))
trunk = self.trunk_conv(self.RRDB_trunk(fea))
fea = fea + trunk
if self.scale >= 2:
fea = F.interpolate(fea, scale_factor=2, mode='nearest')
fea = self.lrelu(self.upconv1(fea))
if self.scale >= 4:
fea = F.interpolate(fea, scale_factor=2, mode='nearest')
fea = self.lrelu(self.upconv2(fea))
out = self.conv_last(self.lrelu(self.HRconv(fea)))
return (out,)
class PixShuffleInitialConv(nn.Module):
def __init__(self, reduction_factor, nf_out):
super(PixShuffleInitialConv, self).__init__()
self.conv = nn.Conv2d(3 * (reduction_factor ** 2), nf_out, 1)
self.unshuffle = PixelUnshuffle(reduction_factor)
def forward(self, x):
(b, f, w, h) = x.shape
# This module can only be applied to input images (with 3 channels)
assert f == 3
x = self.unshuffle(x)
return self.conv(x)
# This class uses a RRDBTrunk to perform processing on an image, then upsamples it.
class PixShuffleRRDB(RRDBBase):
def __init__(self, nf, nb, gc=32, scale=2, rrdb_block_f=None):
super(PixShuffleRRDB, self).__init__()
# This class does a 4x pixel shuffle on the filter count inside the trunk, so nf must be divisible by 16.
assert nf % 16 == 0
# Trunk - does actual processing.
self.trunk = RRDBTrunk(3, nf, nb, gc, 1, rrdb_block_f, PixShuffleInitialConv(4, nf))
self.trunks = [self.trunk]
# Upsampling
pix_nf = int(nf/16)
self.scale = scale
self.upconv1 = nn.Conv2d(pix_nf, pix_nf, 5, 1, padding=2, bias=True)
self.upconv2 = nn.Conv2d(pix_nf, pix_nf, 5, 1, padding=2, bias=True)
self.HRconv = nn.Conv2d(pix_nf, pix_nf, 5, 1, padding=2, bias=True)
self.conv_last = nn.Conv2d(pix_nf, 3, 3, 1, 1, bias=True)
self.pixel_shuffle = nn.PixelShuffle(4)
self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
def forward(self, x):
fea = self.trunk(x)
fea = self.pixel_shuffle(fea)
if self.scale >= 2:
fea = F.interpolate(fea, scale_factor=2, mode='nearest')
fea = self.lrelu(self.upconv1(fea))
if self.scale >= 4:
fea = F.interpolate(fea, scale_factor=2, mode='nearest')
fea = self.lrelu(self.upconv2(fea))
out = self.conv_last(self.lrelu(self.HRconv(fea)))
return (out,)