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New arch: SwitchedResidualGenerator_arch

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The concept here is to use switching to split the generator into two functions:
interpretation and transformation. Transformation is done at the pixel level by
relatively simple conv layers, while interpretation is computed at various levels
by far more complicated conv stacks. The two are merged using the switching
mechanism.

This architecture is far less computationally intensive that RRDB.
This commit is contained in:
James Betker 2020-06-16 11:23:50 -06:00
parent ddfd7f67a0
commit df1046c318
2 changed files with 188 additions and 3 deletions
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184
codes/models/archs/SwitchedResidualGenerator_arch.py Normal file
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@ -0,0 +1,184 @@
import torch
from torch import nn
from switched_conv import BareConvSwitch, compute_attention_specificity
import torch.nn.functional as F
import functools
from models.archs.arch_util import initialize_weights
import torchvision
from torchvision import transforms
class ConvBnLelu(nn.Module):
def __init__(self, filters_in, filters_out, kernel_size=3, stride=1, lelu=True):
super(ConvBnLelu, self).__init__()
padding_map = {1: 0, 3: 1, 5: 2, 7: 3}
assert kernel_size in padding_map.keys()
self.conv = nn.Conv2d(filters_in, filters_out, kernel_size, stride, padding_map[kernel_size])
self.bn = nn.BatchNorm2d(filters_out)
if lelu:
self.lelu = nn.LeakyReLU(negative_slope=.1)
else:
self.lelu = None
def forward(self, x):
x = self.conv(x)
x = self.bn(x)
if self.lelu:
return self.lelu(x)
else:
return x
class ResidualBranch(nn.Module):
def __init__(self, filters_in, filters_out, kernel_size, depth):
super(ResidualBranch, self).__init__()
self.bnconvs = nn.ModuleList([ConvBnLelu(filters_in, filters_out, kernel_size)] +
[ConvBnLelu(filters_out, filters_out, kernel_size) for i in range(depth-2)] +
[ConvBnLelu(filters_out, filters_out, kernel_size, lelu=False)])
self.scale = nn.Parameter(torch.ones(1))
self.bias = nn.Parameter(torch.zeros(1))
def forward(self, x):
for m in self.bnconvs:
x = m.forward(x)
return x * self.scale + self.bias
# VGG-style layer with Conv->BN->Activation->Conv(stride2)->BN->Activation
class HalvingProcessingBlock(nn.Module):
def __init__(self, filters):
super(HalvingProcessingBlock, self).__init__()
self.bnconv1 = ConvBnLelu(filters, filters)
self.bnconv2 = ConvBnLelu(filters, filters * 2, stride=2)
def forward(self, x):
x = self.bnconv1(x)
return self.bnconv2(x)
class SwitchComputer(nn.Module):
def __init__(self, channels_in, filters, transform_block, transform_count, reductions, init_temp=20):
super(SwitchComputer, self).__init__()
self.filter_conv = ConvBnLelu(channels_in, filters)
self.blocks = nn.ModuleList([HalvingProcessingBlock(filters * 2 ** i) for i in range(reductions)])
final_filters = filters * 2 ** reductions
proc_block_filters = max(final_filters // 2, transform_count)
self.proc_switch_conv = ConvBnLelu(final_filters, proc_block_filters)
self.final_switch_conv = nn.Conv2d(proc_block_filters, transform_count, 1, 1, 0)
# Always include the identity transform (all zeros), hence transform_count-10
self.transforms = nn.ModuleList([transform_block() for i in range(transform_count-1)])
# And the switch itself
self.switch = BareConvSwitch(initial_temperature=init_temp)
def forward(self, x, output_attention_weights=False):
xformed = [t.forward(x) for t in self.transforms]
# Append the identity transform.
xformed.append(torch.zeros_like(xformed[0]))
multiplexer = self.filter_conv(x)
for block in self.blocks:
multiplexer = block.forward(multiplexer)
multiplexer = self.proc_switch_conv(multiplexer)
multiplexer = self.final_switch_conv.forward(multiplexer)
# Interpolate the multiplexer across the entire shape of the image.
multiplexer = F.interpolate(multiplexer, size=x.shape[2:], mode='nearest')
return self.switch(xformed, multiplexer, output_attention_weights)
def set_temperature(self, temp):
self.switch.set_attention_temperature(temp)
class SwitchedResidualGenerator(nn.Module):
def __init__(self, switch_filters, initial_temp=20, final_temperature_step=50000):
super(SwitchedResidualGenerator, self).__init__()
self.switch1 = SwitchComputer(3, switch_filters, functools.partial(ResidualBranch, 3, 3, kernel_size=7, depth=3), 4, 4, initial_temp)
self.switch2 = SwitchComputer(3, switch_filters, functools.partial(ResidualBranch, 3, 3, kernel_size=5, depth=3), 8, 3, initial_temp)
self.switch3 = SwitchComputer(3, switch_filters, functools.partial(ResidualBranch, 3, 3, kernel_size=3, depth=3), 16, 2, initial_temp)
self.switch4 = SwitchComputer(3, switch_filters, functools.partial(ResidualBranch, 3, 3, kernel_size=3, depth=2), 32, 1, initial_temp)
initialize_weights([self.switch1, self.switch2, self.switch3, self.switch4], 1)
# Initialize the transforms with a lesser weight, since they are repeatedly added on to the resultant image.
initialize_weights([self.switch1.transforms, self.switch2.transforms, self.switch3.transforms, self.switch4.transforms], .05)
self.init_temperature = initial_temp
self.final_temperature_step = final_temperature_step
self.running_sum = [0, 0, 0, 0]
self.running_count = 0
def forward(self, x):
sw1, self.a1 = self.switch1.forward(x, True)
x = x + sw1
sw2, self.a2 = self.switch2.forward(x, True)
x = x + sw2
sw3, self.a3 = self.switch3.forward(x, True)
x = x + sw3
sw4, self.a4 = self.switch4.forward(x, True)
x = x + sw4
a1mean, _ = compute_attention_specificity(self.a1, 2)
a2mean, _ = compute_attention_specificity(self.a2, 2)
a3mean, _ = compute_attention_specificity(self.a3, 2)
a4mean, _ = compute_attention_specificity(self.a4, 2)
running_sum = [
self.running_sum[0] + a1mean,
self.running_sum[1] + a2mean,
self.running_sum[2] + a3mean,
self.running_sum[3] + a4mean,
]
self.running_count += 1
return (x,)
def set_temperature(self, temp):
self.switch1.set_temperature(temp)
self.switch2.set_temperature(temp)
self.switch3.set_temperature(temp)
self.switch4.set_temperature(temp)
# Copied from torchvision.utils.save_image. Allows specifying pixel format.
def save_image(self, tensor, fp, nrow=8, padding=2,
normalize=False, range=None, scale_each=False, pad_value=0, format=None, pix_format=None):
from PIL import Image
grid = torchvision.utils.make_grid(tensor, nrow=nrow, padding=padding, pad_value=pad_value,
normalize=normalize, range=range, scale_each=scale_each)
# Add 0.5 after unnormalizing to [0, 255] to round to nearest integer
ndarr = grid.mul(255).add_(0.5).clamp_(0, 255).permute(1, 2, 0).to('cpu', torch.uint8).numpy()
im = Image.fromarray(ndarr, mode=pix_format).convert('RGB')
im.save(fp, format=format)
def convert_attention_indices_to_image(self, attention_out, attention_size, step, fname_part="map", l_mult=1.0):
magnitude, indices = torch.topk(attention_out, 1, dim=-1)
magnitude = magnitude.squeeze(3)
indices = indices.squeeze(3)
# indices is an integer tensor (b,w,h) where values are on the range [0,attention_size]
# magnitude is a float tensor (b,w,h) [0,1] representing the magnitude of that attention.
# Use HSV colorspace to show this. Hue is mapped to the indices, Lightness is mapped to intensity,
# Saturation is left fixed.
hue = indices.float() / attention_size
saturation = torch.full_like(hue, .8)
value = magnitude * l_mult
hsv_img = torch.stack([hue, saturation, value], dim=1)
import os
os.makedirs("attention_maps/%s" % (fname_part,), exist_ok=True)
self.save_image(hsv_img, "attention_maps/%s/attention_map_%i.png" % (fname_part, step,), pix_format="HSV")
def get_debug_values(self, step):
# 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)
if step % 250 == 0:
self.convert_attention_indices_to_image(self.a1, 4, step, "a1")
self.convert_attention_indices_to_image(self.a2, 8, step, "a2")
self.convert_attention_indices_to_image(self.a3, 16, step, "a3", 2)
self.convert_attention_indices_to_image(self.a4, 32, step, "a4", 4)
val = {"switch_temperature": temp}
for i in range(len(self.running_sum)):
val["switch_%i_specificity" % (i,)] = self.running_sum[i] / self.running_count
self.running_sum[i] = 0
self.running_count = 0
return val

7
codes/models/networks.py
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@ -7,8 +7,8 @@ import models.archs.FlatProcessorNetNew_arch as FlatProcessorNetNew_arch
import models.archs.RRDBNet_arch as RRDBNet_arch
import models.archs.HighToLowResNet as HighToLowResNet
import models.archs.ResGen_arch as ResGen_arch
import models.archs.biggan_gen_arch as biggan_arch
import models.archs.feature_arch as feature_arch
import models.archs.SwitchedResidualGenerator_arch as SwitchedGen_arch
import functools
# Generator
@ -69,8 +69,9 @@ def define_G(opt, net_key='network_G'):
netG = ResGen_arch.fixup_resnet34_v2(nb_denoiser=opt_net['nb_denoiser'], nb_upsampler=opt_net['nb_upsampler'],
upscale_applications=opt_net['upscale_applications'], num_filters=opt_net['nf'],
inject_noise=opt_net['inject_noise'])
elif which_model == "BigGan":
netG = biggan_arch.biggan_medium(num_filters=opt_net['nf'])
elif which_model == "SwitchedResidualGenerator":
netG = SwitchedGen_arch.SwitchedResidualGenerator(switch_filters=opt_net['nf'], initial_temp=opt_net['temperature'],
final_temperature_step=opt_net['temperature_final_step'])
# image corruption
elif which_model == 'HighToLowResNet':