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Spsr 8

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This commit is contained in:
James Betker 2020-10-02 08:58:15 -06:00
parent c9a9e5c525
commit 35469f08e2
2 changed files with 135 additions and 1 deletions
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131
codes/models/archs/SPSR_arch.py
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@ -458,7 +458,7 @@ class Spsr6(nn.Module):
val["switch_%i_histogram" % (i,)] = hists[i] val["switch_%i_histogram" % (i,)] = hists[i]
return val return val
# Variant of Spsr7 which uses multiplexer blocks that feed off of a reference embedding. Also computes that embedding. # Variant of Spsr6 which uses multiplexer blocks that feed off of a reference embedding. Also computes that embedding.
class Spsr7(nn.Module): class Spsr7(nn.Module):
def __init__(self, in_nc, out_nc, nf, xforms=8, upscale=4, multiplexer_reductions=3, init_temperature=10): def __init__(self, in_nc, out_nc, nf, xforms=8, upscale=4, multiplexer_reductions=3, init_temperature=10):
super(Spsr7, self).__init__() super(Spsr7, self).__init__()
@ -594,3 +594,132 @@ class Spsr7(nn.Module):
val["switch_%i_histogram" % (i,)] = hists[i] val["switch_%i_histogram" % (i,)] = hists[i]
return val 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(Spsr7, 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.sw2, self.sw_grad, self.conjoin_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.sw2(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)
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
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

5
codes/models/networks.py
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@ -73,6 +73,11 @@ 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'], 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, 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) init_temperature=opt_net['temperature'] if 'temperature' in opt_net.keys() else 10)
elif which_model == "spsr8":
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'],
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": elif which_model == "ssgr1":
xforms = opt_net['num_transforms'] if 'num_transforms' in opt_net.keys() else 8 xforms = opt_net['num_transforms'] if 'num_transforms' in opt_net.keys() else 8
netG = ssg.SSGr1(in_nc=3, out_nc=3, nf=opt_net['nf'], xforms=xforms, upscale=opt_net['scale'], netG = ssg.SSGr1(in_nc=3, out_nc=3, nf=opt_net['nf'], xforms=xforms, upscale=opt_net['scale'],