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SPSR9 arch

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takes some of the stuff I learned with SGSR yesterday and applies it to spsr
This commit is contained in:
James Betker 2020-10-05 08:47:51 -06:00
parent 51044929af
commit 2875822024
3 changed files with 141 additions and 157 deletions
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268
codes/models/archs/SPSR_arch.py
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@ -522,7 +522,7 @@ class Spsr7(nn.Module):
self.final_temperature_step = 10000
self.lr = None
def forward(self, x, ref, ref_center, only_return_final_feature_map=False):
def forward(self, x, ref, ref_center, update_attention_norm=True):
# The attention_maps debugger outputs <x>. Save that here.
self.lr = x.detach().cpu()
@ -543,145 +543,12 @@ class Spsr7(nn.Module):
x_grad, a3 = self.sw_grad(x_grad, True, identity=x_grad_identity, att_in=(x_grad, ref_embedding))
x_grad = self.grad_lr_conv(x_grad)
x_grad = self.grad_lr_conv2(x_grad)
if not only_return_final_feature_map:
x_grad_out = self.upsample_grad(x_grad)
x_grad_out = self.grad_branch_output_conv(x_grad_out)
x_grad_out = self.upsample_grad(x_grad)
x_grad_out = self.grad_branch_output_conv(x_grad_out)
x_out = x2
x_out, fea_grad_std = self.conjoin_ref_join(x_out, x_grad)
x_out, a4 = self.conjoin_sw(x_out, True, identity=x2, att_in=(x_out, ref_embedding))
x_out = self.final_lr_conv(x_out)
final_feature_map = x_out
if only_return_final_feature_map:
return final_feature_map
x_out = checkpoint(self.upsample, x_out)
x_out = checkpoint(self.final_hr_conv1, x_out)
x_out = self.final_hr_conv2(x_out)
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, final_feature_map
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)
self.set_temperature(temp)
if step % 500 == 0:
output_path = os.path.join(experiments_path, "attention_maps")
prefix = "amap_%i_a%i_%%i.png"
[save_attention_to_image_rgb(output_path, self.attentions[i], self.transformation_counts, prefix % (step, i), step, output_mag=False) for i in range(len(self.attentions))]
torchvision.utils.save_image(self.lr, os.path.join(experiments_path, "attention_maps", "amap_%i_base_image.png" % (step,)))
def get_debug_values(self, step, net_name):
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,
"grad_branch_feat_intg_std_dev": self.grad_fea_std,
"conjoin_branch_grad_intg_std_dev": self.fea_grad_std}
for i in range(len(means)):
val["switch_%i_specificity" % (i,)] = means[i]
val["switch_%i_histogram" % (i,)] = hists[i]
return val
# Based on Spsr7 but swaps sw2 to the end of the chain. Also re-enables pretransform convs.
class Spsr8(nn.Module):
def __init__(self, in_nc, out_nc, nf, xforms=8, upscale=4, multiplexer_reductions=3, init_temperature=10):
super(Spsr8, self).__init__()
n_upscale = int(math.log(upscale, 2))
# processing the input embedding
self.reference_embedding = ReferenceImageBranch(nf)
# switch options
self.nf = nf
transformation_filters = nf
self.transformation_counts = xforms
multiplx_fn = functools.partial(QueryKeyMultiplexer, transformation_filters, embedding_channels=512, reductions=multiplexer_reductions)
pretransform_fn = functools.partial(ConvGnLelu, transformation_filters, transformation_filters, norm=False, bias=False, weight_init_factor=.1)
transform_fn = functools.partial(MultiConvBlock, transformation_filters, int(transformation_filters * 1.5),
transformation_filters, kernel_size=3, depth=3,
weight_init_factor=.1)
# Feature branch
self.model_fea_conv = ConvGnLelu(in_nc, nf, kernel_size=7, norm=False, activation=False)
self.sw1 = ConfigurableSwitchComputer(transformation_filters, multiplx_fn,
pre_transform_block=pretransform_fn, transform_block=transform_fn,
attention_norm=True,
transform_count=self.transformation_counts, init_temp=init_temperature,
add_scalable_noise_to_transforms=False, feed_transforms_into_multiplexer=True)
# 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.grad_ref_join = ReferenceJoinBlock(nf, residual_weight_init_factor=.3, final_norm=False)
self.sw_grad = ConfigurableSwitchComputer(transformation_filters, multiplx_fn,
pre_transform_block=pretransform_fn, transform_block=transform_fn,
attention_norm=True,
transform_count=self.transformation_counts // 2, init_temp=init_temperature,
add_scalable_noise_to_transforms=False, feed_transforms_into_multiplexer=True)
self.grad_lr_conv = ConvGnLelu(nf, nf, kernel_size=3, norm=False, activation=True, bias=True)
self.grad_lr_conv2 = ConvGnLelu(nf, nf, kernel_size=1, norm=False, activation=True, bias=True)
self.upsample_grad = nn.Sequential(*[UpconvBlock(nf, nf, block=ConvGnLelu, norm=False, activation=True, bias=False) for _ in range(n_upscale)])
self.grad_branch_output_conv = ConvGnLelu(nf, out_nc, kernel_size=1, norm=False, activation=False, bias=True)
# Join branch (grad+fea)
self.noise_ref_join_conjoin = ReferenceJoinBlock(nf, residual_weight_init_factor=.1)
self.conjoin_ref_join = ReferenceJoinBlock(nf, residual_weight_init_factor=.3)
self.conjoin_sw = ConfigurableSwitchComputer(transformation_filters, multiplx_fn,
pre_transform_block=pretransform_fn, transform_block=transform_fn,
attention_norm=True,
transform_count=self.transformation_counts, init_temp=init_temperature,
add_scalable_noise_to_transforms=False, feed_transforms_into_multiplexer=True)
self.final_sw = ConfigurableSwitchComputer(transformation_filters, multiplx_fn,
pre_transform_block=pretransform_fn, transform_block=transform_fn,
attention_norm=True,
transform_count=self.transformation_counts, init_temp=init_temperature,
add_scalable_noise_to_transforms=False, feed_transforms_into_multiplexer=True)
self.final_lr_conv = ConvGnLelu(nf, nf, kernel_size=3, norm=False, activation=True, bias=True)
self.upsample = nn.Sequential(*[UpconvBlock(nf, nf, block=ConvGnLelu, norm=False, activation=True, bias=True) for _ in range(n_upscale)])
self.final_hr_conv1 = ConvGnLelu(nf, nf, kernel_size=3, norm=False, activation=False, bias=True)
self.final_hr_conv2 = ConvGnLelu(nf, out_nc, kernel_size=1, norm=False, activation=False, bias=False)
self.switches = [self.sw1, self.sw_grad, self.conjoin_sw, self.final_sw]
self.attentions = None
self.init_temperature = init_temperature
self.final_temperature_step = 10000
self.lr = None
def forward(self, x, ref, ref_center):
# The attention_maps debugger outputs <x>. Save that here.
self.lr = x.detach().cpu()
x_grad = self.get_g_nopadding(x)
ref_code = self.reference_embedding(ref, ref_center)
ref_embedding = ref_code.view(-1, self.nf * 8, 1, 1).repeat(1, 1, x.shape[2] // 8, x.shape[3] // 8)
x = self.model_fea_conv(x)
x1 = x
x1, a1 = self.sw1(x1, True, identity=x, att_in=(x1, ref_embedding))
x_grad = self.grad_conv(x_grad)
x_grad_identity = x_grad
x_grad, grad_fea_std = self.grad_ref_join(x_grad, x1)
x_grad, a2 = self.sw_grad(x_grad, True, identity=x_grad_identity, att_in=(x_grad, ref_embedding))
x_grad = self.grad_lr_conv(x_grad)
x_grad = self.grad_lr_conv2(x_grad)
x_grad_out = self.upsample_grad(x_grad)
x_grad_out = self.grad_branch_output_conv(x_grad_out)
x_out = x1
x_out, fea_grad_std = self.conjoin_ref_join(x_out, x_grad)
x_out, a3 = self.conjoin_sw(x_out, True, identity=x1, att_in=(x_out, ref_embedding))
x_out, a4 = self.final_sw(x_out, True, identity=x_out, att_in=(x_out, ref_embedding))
x_out = self.final_lr_conv(x_out)
x_out = checkpoint(self.upsample, x_out)
x_out = checkpoint(self.final_hr_conv1, x_out)
@ -719,3 +586,132 @@ class Spsr8(nn.Module):
val["switch_%i_histogram" % (i,)] = hists[i]
return val
class AttentionBlock(nn.Module):
def __init__(self, nf, num_transforms, multiplexer_reductions, init_temperature=10, has_ref=True):
super(AttentionBlock, self).__init__()
self.nf = nf
self.transformation_counts = num_transforms
multiplx_fn = functools.partial(QueryKeyMultiplexer, nf, embedding_channels=512, reductions=multiplexer_reductions)
transform_fn = functools.partial(MultiConvBlock, nf, int(nf * 1.5),
nf, kernel_size=3, depth=4,
weight_init_factor=.1)
if has_ref:
self.ref_join = ReferenceJoinBlock(nf, residual_weight_init_factor=.3, final_norm=False)
else:
self.ref_join = None
self.switch = ConfigurableSwitchComputer(nf, multiplx_fn,
pre_transform_block=None, transform_block=transform_fn,
attention_norm=True,
transform_count=self.transformation_counts, init_temp=init_temperature,
add_scalable_noise_to_transforms=False, feed_transforms_into_multiplexer=True)
def forward(self, x, mplex_ref=None, ref=None):
if self.ref_join is not None:
branch, ref_std = self.ref_join(x, ref)
return self.switch(branch, True, identity=x, att_in=(branch, mplex_ref)) + (ref_std,)
else:
return self.switch(x, True, identity=x, att_in=(x, mplex_ref))
# SPSR7 with incremental improvements and also using the new AttentionBlock to save gpu memory.
class Spsr9(nn.Module):
def __init__(self, in_nc, out_nc, nf, xforms=8, upscale=4, multiplexer_reductions=3, init_temperature=10):
super(Spsr9, self).__init__()
n_upscale = int(math.log(upscale, 2))
self.nf = nf
self.transformation_counts = xforms
# processing the input embedding
self.reference_embedding = ReferenceImageBranch(nf)
# Feature branch
self.model_fea_conv = ConvGnLelu(in_nc, nf, kernel_size=7, norm=False, activation=False)
self.sw1 = AttentionBlock(nf, self.transformation_counts, multiplexer_reductions, init_temperature, False)
self.sw2 = AttentionBlock(nf, self.transformation_counts, multiplexer_reductions, init_temperature, 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 = AttentionBlock(nf, self.transformation_counts // 2, multiplexer_reductions, init_temperature, True)
self.grad_lr_conv = ConvGnLelu(nf, nf, kernel_size=3, norm=False, activation=True, bias=True)
self.grad_lr_conv2 = ConvGnLelu(nf, nf, kernel_size=1, norm=False, activation=True, bias=True)
self.upsample_grad = nn.Sequential(*[UpconvBlock(nf, nf, block=ConvGnLelu, norm=False, activation=True, bias=False) for _ in range(n_upscale)])
self.grad_branch_output_conv = ConvGnLelu(nf, out_nc, kernel_size=1, norm=False, activation=False, bias=True)
# Join branch (grad+fea)
self.conjoin_sw = AttentionBlock(nf, self.transformation_counts, multiplexer_reductions, init_temperature, True)
self.final_lr_conv = ConvGnLelu(nf, nf, kernel_size=3, norm=False, activation=True, bias=True)
self.upsample = nn.Sequential(*[UpconvBlock(nf, nf, block=ConvGnLelu, norm=False, activation=True, bias=True) for _ in range(n_upscale)])
self.final_hr_conv1 = ConvGnLelu(nf, nf, kernel_size=3, norm=False, activation=False, bias=True)
self.final_hr_conv2 = ConvGnLelu(nf, out_nc, kernel_size=1, norm=False, activation=False, bias=False)
self.switches = [self.sw1.switch, self.sw2.switch, self.sw_grad.switch, self.conjoin_sw.switch]
self.attentions = None
self.init_temperature = init_temperature
self.final_temperature_step = 10000
self.lr = None
def forward(self, x, ref, ref_center, update_attention_norm=True):
# The attention_maps debugger outputs <x>. Save that here.
self.lr = x.detach().cpu()
for sw in self.switches:
sw.set_update_attention_norm(update_attention_norm)
x_grad = self.get_g_nopadding(x)
ref_code = checkpoint(self.reference_embedding, ref, ref_center)
ref_embedding = ref_code.view(-1, self.nf * 8, 1, 1).repeat(1, 1, x.shape[2] // 8, x.shape[3] // 8)
x = self.model_fea_conv(x)
x1 = x
x1, a1 = checkpoint(self.sw1, x1, ref_embedding)
x2 = x1
x2, a2 = checkpoint(self.sw2, x2, ref_embedding)
x_grad = self.grad_conv(x_grad)
x_grad_identity = x_grad
x_grad, a3, grad_fea_std = checkpoint(self.sw_grad, x_grad, ref_embedding, x1)
x_grad = self.grad_lr_conv(x_grad)
x_grad = self.grad_lr_conv2(x_grad)
x_grad_out = checkpoint(self.upsample_grad, x_grad)
x_grad_out = checkpoint(self.grad_branch_output_conv, x_grad_out)
x_out = x2
x_out, a4, fea_grad_std = checkpoint(self.conjoin_sw, x_out, ref_embedding, x_grad)
x_out = self.final_lr_conv(x_out)
x_out = checkpoint(self.upsample, x_out)
x_out = checkpoint(self.final_hr_conv1, x_out)
x_out = checkpoint(self.final_hr_conv2, x_out)
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
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)
self.set_temperature(temp)
if step % 500 == 0:
output_path = os.path.join(experiments_path, "attention_maps")
prefix = "amap_%i_a%i_%%i.png"
[save_attention_to_image_rgb(output_path, self.attentions[i], self.transformation_counts, prefix % (step, i), step, output_mag=False) for i in range(len(self.attentions))]
torchvision.utils.save_image(self.lr, os.path.join(experiments_path, "attention_maps", "amap_%i_base_image.png" % (step,)))
def get_debug_values(self, step, net_name):
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,
"grad_branch_feat_intg_std_dev": self.grad_fea_std,
"conjoin_branch_grad_intg_std_dev": self.fea_grad_std}
for i in range(len(means)):
val["switch_%i_specificity" % (i,)] = means[i]
val["switch_%i_histogram" % (i,)] = hists[i]
return val

26
codes/models/archs/SwitchedResidualGenerator_arch.py
View File

@ -7,14 +7,8 @@ from collections import OrderedDict
from models.archs.arch_util import ConvBnLelu, ConvGnSilu, ExpansionBlock, ExpansionBlock2, ConvGnLelu, MultiConvBlock, SiLU
from switched_conv_util import save_attention_to_image_rgb
import os
from utils.util import checkpoint
from models.archs.spinenet_arch import SpineNet
# Set to true to relieve memory pressure by using utils.util in several memory-critical locations.
memory_checkpointing_enabled = True
# VGG-style layer with Conv(stride2)->BN->Activation->Conv->BN->Activation
# Doubles the input filter count.
class HalvingProcessingBlock(nn.Module):
@ -136,19 +130,13 @@ class ConfigurableSwitchComputer(nn.Module):
x = self.pre_transform(*x)
if not isinstance(x, tuple):
x = (x,)
if memory_checkpointing_enabled:
xformed = [checkpoint(t, *x) for t in self.transforms]
else:
xformed = [t(*x) for t in self.transforms]
xformed = [t(*x) for t in self.transforms]
if not isinstance(att_in, tuple):
att_in = (att_in,)
if self.feed_transforms_into_multiplexer:
att_in = att_in + (torch.stack(xformed, dim=1),)
if memory_checkpointing_enabled:
m = checkpoint(self.multiplexer, *att_in)
else:
m = self.multiplexer(*att_in)
m = self.multiplexer(*att_in)
# It is assumed that [xformed] and [m] are collapsed into tensors at this point.
outputs, attention = self.switch(xformed, m, True, self.update_norm)
@ -286,10 +274,10 @@ class BackboneEncoder(nn.Module):
# [ref] will have a 'mask' channel which we cannot use with pretrained spinenet.
ref = ref[:, :3, :, :]
ref_emb = checkpoint(self.ref_spine, ref)[0]
ref_emb = self.ref_spine(ref)[0]
ref_code = gather_2d(ref_emb, ref_center_point // 8) # Divide by 8 to bring the center point to the correct location.
patch = checkpoint(self.patch_spine, x)[0]
patch = self.patch_spine(x)[0]
ref_code_expanded = ref_code.view(-1, 256, 1, 1).repeat(1, 1, patch.shape[2], patch.shape[3])
combined = self.merge_process1(torch.cat([patch, ref_code_expanded], dim=1))
combined = self.merge_process2(combined)
@ -316,7 +304,7 @@ class BackboneEncoderNoRef(nn.Module):
if self.interpolate_first:
x = F.interpolate(x, scale_factor=2, mode="bicubic")
patch = checkpoint(self.patch_spine, x)[0]
patch = self.patch_spine(x)[0]
return patch
@ -332,10 +320,10 @@ class BackboneSpinenetNoHead(nn.Module):
self.merge_process3 = ConvGnSilu(384, 256, kernel_size=1, activation=False, norm=False, bias=True)
def forward(self, x, ref, ref_center_point):
ref_emb = checkpoint(self.ref_spine, ref)[0]
ref_emb = self.ref_spine(ref)[0]
ref_code = gather_2d(ref_emb, ref_center_point // 4) # Divide by 8 to bring the center point to the correct location.
patch = checkpoint(self.patch_spine, x)[0]
patch = self.patch_spine(x)[0]
ref_code_expanded = ref_code.view(-1, 256, 1, 1).repeat(1, 1, patch.shape[2], patch.shape[3])
combined = self.merge_process1(torch.cat([patch, ref_code_expanded], dim=1))
combined = self.merge_process2(combined)

4
codes/models/networks.py
View File

@ -73,9 +73,9 @@ def define_G(opt, net_key='network_G', scale=None):
netG = spsr.Spsr7(in_nc=3, out_nc=3, nf=opt_net['nf'], xforms=xforms, upscale=opt_net['scale'],
multiplexer_reductions=opt_net['multiplexer_reductions'] if 'multiplexer_reductions' in opt_net.keys() else 3,
init_temperature=opt_net['temperature'] if 'temperature' in opt_net.keys() else 10)
elif which_model == "spsr8":
elif which_model == "spsr9":
xforms = opt_net['num_transforms'] if 'num_transforms' in opt_net.keys() else 8
netG = spsr.Spsr8(in_nc=3, out_nc=3, nf=opt_net['nf'], xforms=xforms, upscale=opt_net['scale'],
netG = spsr.Spsr9(in_nc=3, out_nc=3, nf=opt_net['nf'], xforms=xforms, upscale=opt_net['scale'],
multiplexer_reductions=opt_net['multiplexer_reductions'] if 'multiplexer_reductions' in opt_net.keys() else 3,
init_temperature=opt_net['temperature'] if 'temperature' in opt_net.keys() else 10)
elif which_model == "ssgr1":