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LR switched SPSR arch

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This variant doesn't do conv processing at HR, which should save
a ton of memory in inference. Lets see how it works.
This commit is contained in:
James Betker 2020-08-10 13:03:36 -06:00
parent 4e972144ae
commit 59aba1daa7
3 changed files with 132 additions and 2 deletions
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126
codes/models/archs/SPSR_arch.py
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@ -546,3 +546,129 @@ class SwitchedSpsr(nn.Module):
val["switch_%i_specificity" % (i,)] = means[i]
val["switch_%i_histogram" % (i,)] = hists[i]
return val
class SwitchedSpsrLr(nn.Module):
def __init__(self, in_nc, out_nc, nf, upscale=4):
super(SwitchedSpsrLr, self).__init__()
n_upscale = int(math.log(upscale, 2))
# switch options
transformation_filters = nf
switch_filters = nf
switch_reductions = 3
switch_processing_layers = 2
self.transformation_counts = 8
multiplx_fn = functools.partial(ConvBasisMultiplexer, transformation_filters, switch_filters, switch_reductions,
switch_processing_layers, self.transformation_counts)
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=3, 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=10,
add_scalable_noise_to_transforms=True)
self.sw2 = ConfigurableSwitchComputer(transformation_filters, multiplx_fn,
pre_transform_block=pretransform_fn, transform_block=transform_fn,
attention_norm=True,
transform_count=self.transformation_counts, init_temp=10,
add_scalable_noise_to_transforms=True)
self.feature_lr_conv = ConvGnLelu(nf, nf, kernel_size=3, norm=False, activation=False)
self.feature_hr_conv1 = ConvGnLelu(nf, nf, kernel_size=3, norm=False, activation=True, bias=False)
self.feature_hr_conv2 = ConvGnLelu(nf, nf, kernel_size=3, norm=False, activation=False, bias=False)
# Grad branch
self.get_g_nopadding = ImageGradientNoPadding()
self.b_fea_conv = ConvGnLelu(in_nc, nf, kernel_size=3, norm=False, activation=False, bias=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, init_temp=10,
add_scalable_noise_to_transforms=True)
# Upsampling
self.grad_lr_conv = ConvGnLelu(nf, nf, kernel_size=3, norm=False, activation=True, bias=False)
grad_hr_conv1 = ConvGnLelu(nf, nf, kernel_size=3, norm=False, activation=True, bias=False)
grad_hr_conv2 = ConvGnLelu(nf, nf, kernel_size=3, norm=False, activation=False, bias=False)
self.branch_upsample = B.sequential(grad_hr_conv1, grad_hr_conv2)
# Conv used to output grad branch shortcut.
self.grad_branch_output_conv = ConvGnLelu(nf, out_nc, kernel_size=1, norm=False, activation=False, bias=False)
# Conjoin branch.
# Note: "_branch_pretrain" is a special tag used to denote parameters that get pretrained before the rest.
transform_fn_cat = functools.partial(MultiConvBlock, transformation_filters * 2, int(transformation_filters * 1.5),
transformation_filters, kernel_size=3, depth=4,
weight_init_factor=.1)
pretransform_fn_cat = functools.partial(ConvGnLelu, transformation_filters * 2, transformation_filters * 2, norm=False, bias=False, weight_init_factor=.1)
self._branch_pretrain_sw = ConfigurableSwitchComputer(transformation_filters, multiplx_fn,
pre_transform_block=pretransform_fn_cat, transform_block=transform_fn_cat,
attention_norm=True,
transform_count=self.transformation_counts, init_temp=10,
add_scalable_noise_to_transforms=True)
self.upsample = nn.Sequential(*[UpconvBlock(nf, nf, block=ConvGnLelu, norm=False, activation=False, bias=False) for _ in range(n_upscale)])
self.upsample_grad = nn.Sequential(*[UpconvBlock(nf, nf, block=ConvGnLelu, norm=False, activation=False, bias=False) for _ in range(n_upscale)])
self._branch_pretrain_lr_conv = ConvGnLelu(nf, nf, kernel_size=3, norm=False, activation=False)
self._branch_pretrain_HR_conv0 = ConvGnLelu(nf, nf, kernel_size=3, norm=False, activation=True, bias=False)
self._branch_pretrain_HR_conv1 = ConvGnLelu(nf, out_nc, kernel_size=3, norm=False, activation=False, bias=False)
self.switches = [self.sw1, self.sw2, self.sw_grad, self._branch_pretrain_sw]
self.attentions = None
self.init_temperature = 10
self.final_temperature_step = 10000
def forward(self, x):
x_grad = self.get_g_nopadding(x)
x = self.model_fea_conv(x)
x1, a1 = self.sw1(x, True)
x2, a2 = self.sw2(x1, True)
x_fea = self.feature_lr_conv(x2)
x_fea = self.feature_hr_conv1(x_fea)
x_fea = self.feature_hr_conv2(x_fea)
x_b_fea = self.b_fea_conv(x_grad)
x_grad, a3 = self.sw_grad(x_b_fea, att_in=x1, output_attention_weights=True)
x_grad = self.grad_lr_conv(x_grad)
x_grad = self.branch_upsample(x_grad)
x_out_branch = self.upsample_grad(x_grad)
x_out_branch = self.grad_branch_output_conv(x_out_branch)
x__branch_pretrain_cat = torch.cat([x_grad, x_fea], dim=1)
x__branch_pretrain_cat, a4 = self._branch_pretrain_sw(x__branch_pretrain_cat, att_in=x_fea, identity=x_fea, output_attention_weights=True)
x_out = self._branch_pretrain_lr_conv(x__branch_pretrain_cat)
x_out = self.upsample(x_out)
x_out = self._branch_pretrain_HR_conv0(x_out)
x_out = self._branch_pretrain_HR_conv1(x_out)
self.attentions = [a1, a2, a3, a4]
return x_out_branch, x_out, x_grad
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 % 10 == 0:
output_path = os.path.join(experiments_path, "attention_maps", "a%i")
prefix = "attention_map_%i_%%i.png" % (step,)
[save_attention_to_image_rgb(output_path % (i,), self.attentions[i], self.transformation_counts, prefix, step) 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

4
codes/models/archs/SwitchedResidualGenerator_arch.py
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@ -134,11 +134,13 @@ class ConfigurableSwitchComputer(nn.Module):
# depending on its needs.
self.psc_scale = nn.Parameter(torch.full((1,), float(.1)))
def forward(self, x, output_attention_weights=False, att_in=None, fixed_scale=1):
def forward(self, x, output_attention_weights=False, identity=None, att_in=None, fixed_scale=1):
if att_in is None:
att_in = x
if identity is None:
identity = x
if self.add_noise:
rand_feature = torch.randn_like(x) * self.noise_scale
x = x + rand_feature

2
codes/models/networks.py
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@ -113,6 +113,8 @@ def define_G(opt, net_key='network_G'):
nb=opt_net['nb'], upscale=opt_net['scale'])
elif which_model == "spsr_switched":
netG = spsr.SwitchedSpsr(in_nc=3, out_nc=3, nf=opt_net['nf'], upscale=opt_net['scale'])
elif which_model == "spsr_switched_lr":
netG = spsr.SwitchedSpsrLr(in_nc=3, out_nc=3, nf=opt_net['nf'], upscale=opt_net['scale'])
# image corruption
elif which_model == 'HighToLowResNet':