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Add SRG4

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Back to the idea that maybe what we need is a hybrid
approach between pure switches and RDB.
This commit is contained in:
James Betker 2020-07-24 20:32:49 -06:00
parent 3320ad685f
commit e6e91a1d75
3 changed files with 110 additions and 2 deletions
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1
.idea/misc.xml
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@ -3,4 +3,5 @@
<component name="JavaScriptSettings">
<option name="languageLevel" value="ES6" />
</component>
<component name="ProjectRootManager" version="2" project-jdk-name="Python 3.7 (torch-1.4-venv) (2)" project-jdk-type="Python SDK" />
</project>

100
codes/models/archs/SwitchedResidualGenerator_arch.py
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@ -5,7 +5,7 @@ import torch.nn.functional as F
import functools
from collections import OrderedDict
from models.archs.arch_util import ConvBnLelu, ConvGnSilu, ExpansionBlock
from models.archs.RRDBNet_arch import ResidualDenseBlock_5C
from models.archs.RRDBNet_arch import ResidualDenseBlock_5C, RRDB
from models.archs.spinenet_arch import SpineNet
from switched_conv_util import save_attention_to_image
@ -117,7 +117,10 @@ class ConfigurableSwitchComputer(nn.Module):
tc = transform_count
self.multiplexer = multiplexer_net(tc)
if pre_transform_block:
self.pre_transform = pre_transform_block()
else:
self.pre_transform = None
self.transforms = nn.ModuleList([transform_block() for _ in range(transform_count)])
self.add_noise = add_scalable_noise_to_transforms
self.noise_scale = nn.Parameter(torch.full((1,), float(1e-3)))
@ -237,6 +240,101 @@ class ConfigurableSwitchedResidualGenerator2(nn.Module):
val["switch_%i_histogram" % (i,)] = hists[i]
return val
# Equivalent to SRG2 - Uses RDB blocks in between two switches.
class ConfigurableSwitchedResidualGenerator4(nn.Module):
def __init__(self, switch_filters, switch_reductions, switch_processing_layers, trans_counts, trans_kernel_sizes,
trans_layers, transformation_filters, attention_norm, initial_temp=20, final_temperature_step=50000, heightened_temp_min=1,
heightened_final_step=50000, upsample_factor=1,
add_scalable_noise_to_transforms=False):
super(ConfigurableSwitchedResidualGenerator4, self).__init__()
self.initial_conv = ConvBnLelu(3, transformation_filters, norm=False, activation=False, bias=True)
self.upconv1 = ConvBnLelu(transformation_filters, transformation_filters, norm=False, bias=True)
self.upconv2 = ConvBnLelu(transformation_filters, transformation_filters, norm=False, bias=True)
self.hr_conv = ConvBnLelu(transformation_filters, transformation_filters, norm=False, bias=True)
multiplx_fn = functools.partial(ConvBasisMultiplexer, transformation_filters, switch_filters, switch_reductions,
switch_processing_layers, trans_counts)
transform_fn = functools.partial(MultiConvBlock, transformation_filters, int(transformation_filters * 1.5),
transformation_filters, kernel_size=trans_kernel_sizes, depth=trans_layers,
weight_init_factor=.1)
self.rdb1 = RRDB(transformation_filters)
self.sw1 = ConfigurableSwitchComputer(transformation_filters, multiplx_fn,
pre_transform_block=None, transform_block=transform_fn,
attention_norm=attention_norm,
transform_count=trans_counts, init_temp=initial_temp,
add_scalable_noise_to_transforms=add_scalable_noise_to_transforms)
self.rdb2 = RRDB(transformation_filters)
self.sw1 = ConfigurableSwitchComputer(transformation_filters, multiplx_fn,
pre_transform_block=None, transform_block=transform_fn,
attention_norm=attention_norm,
transform_count=trans_counts, init_temp=initial_temp,
add_scalable_noise_to_transforms=add_scalable_noise_to_transforms)
self.rdb3 = RRDB(transformation_filters)
self.final_conv = ConvBnLelu(transformation_filters, 3, norm=False, activation=False, bias=True)
self.transformation_counts = trans_counts
self.init_temperature = initial_temp
self.final_temperature_step = final_temperature_step
self.heightened_temp_min = heightened_temp_min
self.heightened_final_step = heightened_final_step
self.attentions = None
self.upsample_factor = upsample_factor
assert self.upsample_factor == 2 or self.upsample_factor == 4
def forward(self, x):
# This is a common bug when evaluating SRG2 generators. It needs to be configured properly in eval mode. Just fail.
if not self.train:
assert self.switches[0].switch.temperature == 1
x = self.initial_conv(x)
x = self.rdb1(x)
x = self.sw1(x, True)
x = self.rdb2(x)
x = self.sw2(x, True)
x = self.rdb3(x)
x = self.upconv1(F.interpolate(x, scale_factor=2, mode="nearest"))
if self.upsample_factor > 2:
x = F.interpolate(x, scale_factor=2, mode="nearest")
x = self.upconv2(x)
x = self.final_conv(self.hr_conv(x))
return x, x
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)
if temp == 1 and self.heightened_final_step and step > self.final_temperature_step and \
self.heightened_final_step != 1:
# Once the temperature passes (1) it enters an inverted curve to match the linear curve from above.
# without this, the attention specificity "spikes" incredibly fast in the last few iterations.
h_steps_total = self.heightened_final_step - self.final_temperature_step
h_steps_current = min(step - self.final_temperature_step, h_steps_total)
# The "gap" will represent the steps that need to be traveled as a linear function.
h_gap = 1 / self.heightened_temp_min
temp = h_gap * h_steps_current / h_steps_total
# Invert temperature to represent reality on this side of the curve
temp = 1 / temp
self.set_temperature(temp)
if step % 50 == 0:
[save_attention_to_image(experiments_path, self.attentions[i], self.transformation_counts, step, "a%i" % (i+1,), l_mult=10) for i in range(len(self.attentions))]
def get_debug_values(self, step):
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}
for i in range(len(means)):
val["switch_%i_specificity" % (i,)] = means[i]
val["switch_%i_histogram" % (i,)] = hists[i]
return val
class Interpolate(nn.Module):
def __init__(self, factor):
super(Interpolate, self).__init__()

9
codes/models/networks.py
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@ -79,6 +79,15 @@ def define_G(opt, net_key='network_G'):
initial_temp=opt_net['temperature'], final_temperature_step=opt_net['temperature_final_step'],
heightened_temp_min=opt_net['heightened_temp_min'], heightened_final_step=opt_net['heightened_final_step'],
upsample_factor=scale, add_scalable_noise_to_transforms=opt_net['add_noise'])
elif which_model == "ConfigurableSwitchedResidualGenerator4":
netG = SwitchedGen_arch.ConfigurableSwitchedResidualGenerator4(switch_filters=opt_net['switch_filters'],
switch_reductions=opt_net['switch_reductions'],
switch_processing_layers=opt_net['switch_processing_layers'], trans_counts=opt_net['trans_counts'],
trans_kernel_sizes=opt_net['trans_kernel_sizes'], trans_layers=opt_net['trans_layers'],
transformation_filters=opt_net['transformation_filters'], attention_norm=opt_net['attention_norm'],
initial_temp=opt_net['temperature'], final_temperature_step=opt_net['temperature_final_step'],
heightened_temp_min=opt_net['heightened_temp_min'], heightened_final_step=opt_net['heightened_final_step'],
upsample_factor=scale, add_scalable_noise_to_transforms=opt_net['add_noise'])
elif which_model == "ProgressiveSRG2":
netG = psrg.GrowingSRGBase(progressive_step_schedule=opt_net['schedule'], switch_reductions=opt_net['reductions'],
growth_fade_in_steps=opt_net['fade_in_steps'], switch_filters=opt_net['switch_filters'],