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SSGSimpler network

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This commit is contained in:
James Betker 2020-10-15 17:18:44 -06:00
parent 1ba01d69b5
commit 6f8705e8cb
4 changed files with 148 additions and 10 deletions
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146
codes/models/archs/StructuredSwitchedGenerator.py
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@ -1,17 +1,16 @@
import math
import functools
from models.archs.arch_util import MultiConvBlock, ConvGnLelu, ConvGnSilu, ReferenceJoinBlock
from models.archs.SwitchedResidualGenerator_arch import ConfigurableSwitchComputer, gather_2d, SwitchModelBase
from models.archs.SPSR_arch import ImageGradientNoPadding
from torch import nn
import math
import torch
import torch.nn.functional as F
from switched_conv.switched_conv_util import save_attention_to_image_rgb
from switched_conv.switched_conv import compute_attention_specificity
import os
import torchvision
from torch import nn
from models.archs.SPSR_arch import ImageGradientNoPadding
from models.archs.SwitchedResidualGenerator_arch import ConfigurableSwitchComputer, gather_2d, SwitchModelBase
from models.archs.arch_util import MultiConvBlock, ConvGnLelu, ConvGnSilu, ReferenceJoinBlock
from utils.util import checkpoint
# VGG-style layer with Conv(stride2)->BN->Activation->Conv->BN->Activation
# Doubles the input filter count.
class HalvingProcessingBlock(nn.Module):
@ -446,3 +445,132 @@ class StackedSwitchGenerator2xTeco(SwitchModelBase):
self.attentions = [a1, a3, a3, a4, a5]
return x_out,
class SimplePyramidMultiplexer(nn.Module):
def __init__(self, nf, transforms):
super(SimplePyramidMultiplexer, self).__init__()
# Blocks used to create the query
reductions = 3
self.input_process = ConvGnSilu(nf, nf, activation=True, norm=False, bias=True)
self.reduction_blocks = nn.ModuleList([HalvingProcessingBlock(int(nf * 1.5 ** i), factor=1.5)
for i in range(reductions)])
reduction_filters = int(nf * 1.5 ** reductions)
self.processing_blocks = nn.Sequential(
ConvGnSilu(reduction_filters, reduction_filters, kernel_size=3, activation=True, norm=True, bias=False),
ConvGnSilu(reduction_filters, reduction_filters, kernel_size=3, activation=True, norm=True, bias=False))
self.expansion_blocks = nn.ModuleList([ExpansionBlock2(int(reduction_filters // (1.5 ** i)), factor=1.5)
for i in range(reductions)])
self.cbl1 = ConvGnSilu(nf, nf // 2, kernel_size=1, norm=False, bias=False)
self.cbl2 = ConvGnSilu(nf // 2, transforms, kernel_size=1, norm=False, bias=False)
def forward(self, x):
q = self.input_process(x)
reduction_identities = []
for b in self.reduction_blocks:
reduction_identities.append(q)
q = b(q)
q = self.processing_blocks(q)
for i, b in enumerate(self.expansion_blocks):
q = b(q, reduction_identities[-i - 1])
q = self.cbl1(q)
q = self.cbl2(q)
return q
class SimplerSwitchWithReference(nn.Module):
def __init__(self, nf, num_transforms, init_temperature=10, has_ref=True):
super(SimplerSwitchWithReference, self).__init__()
self.nf = nf
self.transformation_counts = num_transforms
multiplx_fn = functools.partial(SimplePyramidMultiplexer, nf)
pretransform = functools.partial(ConvGnLelu, nf, int(nf*1.5), kernel_size=3, bias=False, norm=False, activation=True, weight_init_factor=.1)
transform_fn = functools.partial(ConvGnLelu, int(nf * 1.5), int(nf * 1.5), kernel_size=3, bias=False, norm=False, activation=True, weight_init_factor=.1)
posttransform = ConvGnLelu(int(nf*1.5), nf, kernel_size=3, bias=False, norm=False, activation=True, weight_init_factor=.1)
if has_ref:
self.ref_join = ReferenceJoinBlock(nf, residual_weight_init_factor=.3, final_norm=False, kernel_size=1, depth=2)
else:
self.ref_join = None
self.switch = ConfigurableSwitchComputer(nf, multiplx_fn,
pre_transform_block=pretransform, transform_block=transform_fn,
post_transform_block=posttransform,
attention_norm=True,
transform_count=self.transformation_counts, init_temp=init_temperature,
add_scalable_noise_to_transforms=False, feed_transforms_into_multiplexer=False)
def forward(self, x, ref=None):
if self.ref_join is not None:
branch, ref_std = self.ref_join(x, ref)
return self.switch(branch, identity=x) + (ref_std,)
else:
return self.switch(x, identity=x)
class SsgSimpler(SwitchModelBase):
def __init__(self, in_nc, out_nc, nf, xforms=8, init_temperature=10, recurrent=False):
super(SsgSimpler, self).__init__(init_temperature, 10000)
self.nf = nf
# processing the input embedding
if recurrent:
self.recurrent = True
self.recurrent_process = ConvGnLelu(in_nc, nf, kernel_size=3, stride=2, norm=False, bias=True, activation=False)
self.recurrent_join = ReferenceJoinBlock(nf, residual_weight_init_factor=.01, final_norm=False, kernel_size=1, depth=3, join=False)
else:
self.recurrent = False
# Feature branch
self.model_fea_conv = ConvGnLelu(in_nc, nf, kernel_size=7, norm=False, activation=False)
self.sw1 = SimplerSwitchWithReference(nf, xforms, init_temperature, has_ref=False)
self.sw2 = SimplerSwitchWithReference(nf, xforms, init_temperature, has_ref=False)
# Grad branch. Note - groupnorm on this branch is REALLY bad. Avoid it like the plague.
self.get_g_nopadding = ImageGradientNoPadding()
self.grad_conv = ConvGnLelu(in_nc, nf, kernel_size=7, norm=False, activation=False, bias=False)
self.sw_grad = SimplerSwitchWithReference(nf, xforms // 2, init_temperature, has_ref=True)
self.grad_lr_conv = ConvGnLelu(nf, nf, kernel_size=3, norm=False, activation=True, bias=True)
self.upsample_grad = UpconvBlock(nf, nf // 2, block=ConvGnLelu, norm=False, activation=True, bias=False)
self.grad_branch_output_conv = ConvGnLelu(nf // 2, out_nc, kernel_size=1, norm=False, activation=False, bias=True)
# Join branch (grad+fea)
self.conjoin_sw = SimplerSwitchWithReference(nf, xforms, init_temperature, has_ref=True)
self.final_lr_conv = ConvGnLelu(nf, nf, kernel_size=3, norm=False, activation=True, bias=True)
self.upsample = UpconvBlock(nf, nf // 2, block=ConvGnLelu, norm=False, activation=True, bias=True)
self.final_hr_conv1 = ConvGnLelu(nf // 2, nf // 2, kernel_size=3, norm=False, activation=False, bias=True)
self.final_hr_conv2 = ConvGnLelu(nf // 2, out_nc, kernel_size=3, norm=False, activation=False, bias=False)
self.switches = [self.sw1.switch, self.sw2.switch, self.sw_grad.switch, self.conjoin_sw.switch]
def forward(self, x, save_attentions=True, recurrent=None):
# The attention_maps debugger outputs <x>. Save that here.
self.lr = x.detach().cpu()
# If we're not saving attention, we also shouldn't be updating the attention norm. This is because the attention
# norm should only be getting updates with new data, not recurrent generator sampling.
for sw in self.switches:
sw.set_update_attention_norm(save_attentions)
x1 = self.model_fea_conv(x)
if self.recurrent:
rec = self.recurrent_process(recurrent)
x1, recurrent_std = self.recurrent_join(x1, rec)
x1, a1 = checkpoint(self.sw1, x1)
x2, a2 = checkpoint(self.sw2, x1)
x_grad = self.get_g_nopadding(x)
x_grad = self.grad_conv(x_grad)
x_grad, a3, grad_fea_std = checkpoint(self.sw_grad, x_grad, x1)
x_grad = checkpoint(self.grad_lr_conv, x_grad)
x_grad_out = checkpoint(self.upsample_grad, x_grad)
x_grad_out = checkpoint(self.grad_branch_output_conv, x_grad_out)
x3, a4, fea_grad_std = checkpoint(self.conjoin_sw, x2, x_grad)
x_out = checkpoint(self.final_lr_conv, x3)
x_out = checkpoint(self.upsample, x_out)
x_out = checkpoint(self.final_hr_conv2, x_out)
if save_attentions:
self.attentions = [a1, a2, a3, a4]
self.grad_fea_std = grad_fea_std.detach().cpu()
self.fea_grad_std = fea_grad_std.detach().cpu()
return x_grad_out, x_out

6
codes/models/archs/SwitchedResidualGenerator_arch.py
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@ -79,6 +79,7 @@ def gather_2d(input, index):
class ConfigurableSwitchComputer(nn.Module):
def __init__(self, base_filters, multiplexer_net, pre_transform_block, transform_block, transform_count, attention_norm,
post_transform_block=None,
init_temp=20, add_scalable_noise_to_transforms=False, feed_transforms_into_multiplexer=False, post_switch_conv=True,
anorm_multiplier=16):
super(ConfigurableSwitchComputer, self).__init__()
@ -98,6 +99,8 @@ class ConfigurableSwitchComputer(nn.Module):
# And the switch itself, including learned scalars
self.switch = BareConvSwitch(initial_temperature=init_temp, attention_norm=AttentionNorm(transform_count, accumulator_size=anorm_multiplier * transform_count) if attention_norm else None)
self.switch_scale = nn.Parameter(torch.full((1,), float(1)))
if post_transform_block is not None:
self.post_transform_block = post_transform_block
if post_switch_conv:
self.post_switch_conv = ConvBnLelu(base_filters, base_filters, norm=False, bias=True)
# The post_switch_conv gets a low scale initially. The network can decide to magnify it (or not)
@ -154,6 +157,9 @@ class ConfigurableSwitchComputer(nn.Module):
# It is assumed that [xformed] and [m] are collapsed into tensors at this point.
outputs, attention, att_logits = self.switch(xformed, m, True, self.update_norm, output_attention_logits=True)
if self.post_transform_block is not None:
outputs = self.post_transform_block(outputs)
outputs = identity + outputs * self.switch_scale * fixed_scale
if self.post_switch_conv is not None:
outputs = outputs + self.post_switch_conv(outputs) * self.psc_scale * fixed_scale

4
codes/models/networks.py
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@ -109,6 +109,10 @@ def define_G(opt, net_key='network_G', scale=None):
xforms = opt_net['num_transforms'] if 'num_transforms' in opt_net.keys() else 8
netG = ssg.SSGDeep(in_nc=3, out_nc=3, nf=opt_net['nf'], xforms=xforms, upscale=opt_net['scale'],
init_temperature=opt_net['temperature'] if 'temperature' in opt_net.keys() else 10)
elif which_model == 'ssg_simpler':
xforms = opt_net['num_transforms'] if 'num_transforms' in opt_net.keys() else 8
netG = ssg.SsgSimpler(in_nc=3, out_nc=3, nf=opt_net['nf'], xforms=xforms,
init_temperature=opt_net['temperature'] if 'temperature' in opt_net.keys() else 10)
elif which_model == 'ssg_teco':
netG = ssg.StackedSwitchGenerator2xTeco(nf=opt_net['nf'], xforms=opt_net['num_transforms'], init_temperature=opt_net['temperature'] if 'temperature' in opt_net.keys() else 10)
elif which_model == 'big_switch':

2
codes/train.py
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@ -30,7 +30,7 @@ def init_dist(backend='nccl', **kwargs):
def main():
#### options
parser = argparse.ArgumentParser()
parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/train_exd_imgset_bigswitch.yml')
parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/train_exd_imgset_ssgsimpler.yml')
parser.add_argument('--launcher', choices=['none', 'pytorch'], default='none', help='job launcher')
parser.add_argument('--local_rank', type=int, default=0)
args = parser.parse_args()