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This commit is contained in:
James Betker 2020-10-15 10:13:06 -06:00
parent 1dc0b05428
commit 920865defb
3 changed files with 200 additions and 11 deletions
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126
codes/models/archs/SPSR_arch.py
View File

@ -10,7 +10,7 @@ from utils.util import checkpoint
from models.archs import SPSR_util as B
from models.archs.SwitchedResidualGenerator_arch import ConfigurableSwitchComputer, ReferenceImageBranch, \
QueryKeyMultiplexer, QueryKeyPyramidMultiplexer
QueryKeyMultiplexer, QueryKeyPyramidMultiplexer, ConvBasisMultiplexer
from models.archs.arch_util import ConvGnLelu, UpconvBlock, MultiConvBlock, ReferenceJoinBlock
from switched_conv.switched_conv import compute_attention_specificity
from switched_conv.switched_conv_util import save_attention_to_image_rgb
@ -225,8 +225,6 @@ class Spsr5(nn.Module):
attention_norm=True,
transform_count=self.transformation_counts, init_temp=init_temperature,
add_scalable_noise_to_transforms=False, feed_transforms_into_multiplexer=True)
self.feature_lr_conv = ConvGnLelu(nf, nf, kernel_size=3, norm=True, activation=False)
self.feature_lr_conv2 = ConvGnLelu(nf, nf, kernel_size=3, norm=False, activation=False, bias=False)
# Grad branch. Note - groupnorm on this branch is REALLY bad. Avoid it like the plague.
self.get_g_nopadding = ImageGradientNoPadding()
@ -726,3 +724,125 @@ class Spsr9(nn.Module):
val["switch_%i_specificity" % (i,)] = means[i]
val["switch_%i_histogram" % (i,)] = hists[i]
return val
class SwitchedSpsr(nn.Module):
def __init__(self, in_nc, nf, xforms=8, upscale=4, init_temperature=10):
super(SwitchedSpsr, 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 = xforms
multiplx_fn = functools.partial(ConvBasisMultiplexer, transformation_filters, switch_filters, switch_reductions,
switch_processing_layers, self.transformation_counts, use_exp2=True)
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=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=init_temperature,
add_scalable_noise_to_transforms=True)
self.feature_lr_conv = ConvGnLelu(nf, nf, kernel_size=3, norm=True, activation=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=7, norm=False, activation=False, bias=False)
mplex_grad = functools.partial(ConvBasisMultiplexer, nf * 2, nf * 2, switch_reductions,
switch_processing_layers, self.transformation_counts // 2, use_exp2=True)
self.sw_grad = ConfigurableSwitchComputer(transformation_filters, mplex_grad,
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=True)
# Upsampling
self.grad_lr_conv = ConvGnLelu(nf, nf, kernel_size=3, norm=False, activation=True, bias=False)
self.grad_hr_conv = ConvGnLelu(nf, nf, kernel_size=3, norm=False, activation=False, bias=False)
# Conv used to output grad branch shortcut.
self.grad_branch_output_conv = ConvGnLelu(nf, 3, 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=init_temperature,
add_scalable_noise_to_transforms=True)
self.upsample = nn.Sequential(*[UpconvBlock(nf, nf, block=ConvGnLelu, norm=False, activation=False, bias=True) for _ in range(n_upscale)])
self.upsample_grad = nn.Sequential(*[UpconvBlock(nf, nf, block=ConvGnLelu, norm=False, activation=False, bias=True) for _ in range(n_upscale)])
self.final_lr_conv = ConvGnLelu(nf, nf, kernel_size=3, norm=True, activation=False)
self.final_hr_conv1 = ConvGnLelu(nf, nf, kernel_size=3, norm=True, activation=True, bias=True)
self.final_hr_conv2 = ConvGnLelu(nf, 3, 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 = init_temperature
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, do_checkpointing=True)
x2, a2 = self.sw2(x1, do_checkpointing=True)
x_fea = self.feature_lr_conv(x2)
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=torch.cat([x1, x_b_fea], dim=1), output_attention_weights=True, do_checkpointing=True)
x_grad = checkpoint(self.grad_lr_conv, x_grad)
x_grad = checkpoint(self.grad_hr_conv, x_grad)
x_out_branch = checkpoint(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 = checkpoint(self.final_lr_conv, x__branch_pretrain_cat)
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]
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 % 200 == 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, net):
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

80
codes/models/archs/SwitchedResidualGenerator_arch.py
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@ -79,7 +79,8 @@ 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,
init_temp=20, add_scalable_noise_to_transforms=False, feed_transforms_into_multiplexer=False):
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__()
tc = transform_count
@ -95,12 +96,15 @@ class ConfigurableSwitchComputer(nn.Module):
self.noise_scale = nn.Parameter(torch.full((1,), float(1e-3)))
# And the switch itself, including learned scalars
self.switch = BareConvSwitch(initial_temperature=init_temp, attention_norm=AttentionNorm(transform_count, accumulator_size=16 * transform_count) if attention_norm else None)
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)))
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)
# depending on its needs.
self.psc_scale = nn.Parameter(torch.full((1,), float(.1)))
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)
# depending on its needs.
self.psc_scale = nn.Parameter(torch.full((1,), float(.1)))
else:
self.post_switch_conv = None
self.update_norm = True
def set_update_attention_norm(self, set_val):
@ -151,7 +155,8 @@ 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)
outputs = identity + outputs * self.switch_scale * fixed_scale
outputs = outputs + self.post_switch_conv(outputs) * self.psc_scale * fixed_scale
if self.post_switch_conv is not None:
outputs = outputs + self.post_switch_conv(outputs) * self.psc_scale * fixed_scale
if output_attention_weights:
if output_att_logits:
return outputs, attention, att_logits
@ -642,7 +647,8 @@ class TheBigSwitch(SwitchModelBase):
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)
add_scalable_noise_to_transforms=False, feed_transforms_into_multiplexer=True,
anorm_multiplier=128)
self.switches = [self.switch]
self.final_lr_conv = ConvGnLelu(nf, nf, kernel_size=3, norm=False, activation=True, bias=True)
@ -670,6 +676,64 @@ class TheBigSwitch(SwitchModelBase):
return x_out,
class ArtistMultiplexer(nn.Module):
def __init__(self, in_nc, nf, multiplexer_channels):
super(ArtistMultiplexer, self).__init__()
self.spine = SpineNet(arch='96', output_level=[3], double_reduce_early=False)
self.spine_red_proc = ConvGnSilu(256, nf, kernel_size=1, activation=False, norm=False, bias=False)
self.fea_tail = ConvGnSilu(in_nc, nf, kernel_size=7, bias=True, norm=False, activation=False)
self.tail_proc = make_res_layer(BasicBlock, nf, nf, 2)
self.tail_join = ReferenceJoinBlock(nf)
self.reduce = ConvGnSilu(nf, nf // 2, kernel_size=1, activation=True, norm=True, bias=False)
self.last_process = ConvGnSilu(nf // 2, nf // 2, kernel_size=1, activation=True, norm=False, bias=False)
self.to_attention = ConvGnSilu(nf // 2, multiplexer_channels, kernel_size=1, activation=False, norm=False, bias=False)
def forward(self, x, transformations):
s = self.spine(x)[0]
tail = self.fea_tail(x)
tail = self.tail_proc(tail)
q = F.interpolate(s, scale_factor=2, mode='nearest')
q = self.spine_red_proc(q)
q, _ = self.tail_join(q, tail)
q = self.reduce(q)
q = F.interpolate(q, scale_factor=2, mode='nearest')
return self.to_attention(self.last_process(q))
class ArtistGen(SwitchModelBase):
def __init__(self, in_nc, nf, xforms=16, upscale=2, init_temperature=10):
super(ArtistGen, self).__init__(init_temperature, 10000)
self.nf = nf
self.transformation_counts = xforms
multiplx_fn = functools.partial(ArtistMultiplexer, in_nc, nf)
transform_fn = functools.partial(MultiConvBlock, in_nc, int(in_nc * 2), in_nc, kernel_size=3, depth=4, weight_init_factor=.1)
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,
anorm_multiplier=128, post_switch_conv=False)
self.switches = [self.switch]
def forward(self, x, save_attentions=True):
# 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)
up = F.interpolate(x, scale_factor=2, mode="bicubic")
out, a1, att_logits = self.switch(up, att_in=x, do_checkpointing=True, output_att_logits=True)
if save_attentions:
self.attentions = [a1]
return out, att_logits.permute(0,3,1,2)
if __name__ == '__main__':
tbs = TheBigSwitch(3, 64)
x = torch.randn(4,3,64,64)

5
codes/models/networks.py
View File

@ -67,6 +67,8 @@ def define_G(opt, net_key='network_G', scale=None):
elif which_model == 'spsr_net_improved':
netG = spsr.SPSRNetSimplified(in_nc=opt_net['in_nc'], out_nc=opt_net['out_nc'], nf=opt_net['nf'],
nb=opt_net['nb'], upscale=opt_net['scale'])
elif which_model == "spsr_switched":
netG = spsr.SwitchedSpsr(in_nc=3, nf=opt_net['nf'], upscale=opt_net['scale'], init_temperature=opt_net['temperature'])
elif which_model == "spsr5":
xforms = opt_net['num_transforms'] if 'num_transforms' in opt_net.keys() else 8
netG = spsr.Spsr5(in_nc=3, out_nc=3, nf=opt_net['nf'], xforms=xforms, upscale=opt_net['scale'],
@ -112,6 +114,9 @@ def define_G(opt, net_key='network_G', scale=None):
elif which_model == 'big_switch':
netG = SwitchedGen_arch.TheBigSwitch(opt_net['in_nc'], nf=opt_net['nf'], xforms=opt_net['num_transforms'], upscale=opt_net['scale'],
init_temperature=opt_net['temperature'])
elif which_model == 'artist':
netG = SwitchedGen_arch.ArtistGen(opt_net['in_nc'], nf=opt_net['nf'], xforms=opt_net['num_transforms'], upscale=opt_net['scale'],
init_temperature=opt_net['temperature'])
elif which_model == "flownet2":
from models.flownet2.models import FlowNet2
ld = torch.load(opt_net['load_path'])