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StackedSwitchedGenerator_5lyr

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This commit is contained in:
James Betker 2020-10-06 20:39:32 -06:00
parent 6217b48e3f
commit 2f2e3f33f8
2 changed files with 97 additions and 10 deletions
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103
codes/models/archs/StructuredSwitchedGenerator.py
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@ -151,6 +151,7 @@ class SwitchWithReference(nn.Module):
else:
return self.switch(x, True, identity=x, att_in=(x, mplex_ref))
class SSGr1(nn.Module):
def __init__(self, in_nc, out_nc, nf, xforms=8, upscale=4, init_temperature=10):
super(SSGr1, self).__init__()
@ -233,16 +234,17 @@ class SSGr1(nn.Module):
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]
if self.attentions:
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
@ -414,3 +416,84 @@ class SSGDeep(nn.Module):
val["switch_%i_specificity" % (i,)] = means[i]
val["switch_%i_histogram" % (i,)] = hists[i]
return val
class StackedSwitchGenerator5Layer(nn.Module):
def __init__(self, in_nc, out_nc, nf, xforms=8, upscale=4, init_temperature=10):
super(StackedSwitchGenerator5Layer, self).__init__()
n_upscale = int(math.log(upscale, 2))
self.nf = nf
# processing the input embedding
self.reference_embedding = ReferenceImageBranch(nf)
# Feature branch
self.model_fea_conv = ConvGnLelu(in_nc, nf, kernel_size=3, norm=False, activation=False)
self.sw1 = SwitchWithReference(nf, xforms, init_temperature, has_ref=False)
self.sw2 = SwitchWithReference(nf, xforms // 2, init_temperature, has_ref=False)
self.sw3 = SwitchWithReference(nf, xforms // 2, init_temperature, has_ref=False)
self.sw4 = SwitchWithReference(nf, xforms // 2, init_temperature, has_ref=False)
self.sw5 = SwitchWithReference(nf, xforms, init_temperature, has_ref=False)
self.switches = [self.sw1.switch, self.sw2.switch, self.sw3.switch, self.sw4.switch, self.sw5.switch]
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.attentions = None
self.lr = None
self.init_temperature = init_temperature
self.final_temperature_step = 10000
def forward(self, x, ref, ref_center, 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)
ref_code = checkpoint(self.reference_embedding, ref, ref_center)
ref_embedding = ref_code.view(-1, ref_code.shape[1], 1, 1).repeat(1, 1, x.shape[2] // 8, x.shape[3] // 8)
x = self.model_fea_conv(x)
x1, a1 = checkpoint(self.sw1, x, ref_embedding)
x2, a2 = checkpoint(self.sw2, x1, ref_embedding)
x3, a3 = checkpoint(self.sw3, x2, ref_embedding)
x4, a4 = checkpoint(self.sw4, x3, ref_embedding)
x5, a5 = checkpoint(self.sw5, x4, ref_embedding)
x_out = checkpoint(self.final_lr_conv, x5)
x_out = checkpoint(self.upsample, x_out)
x_out = checkpoint(self.final_hr_conv2, x_out)
if save_attentions:
self.attentions = [a1, a3, a3, a4, a5]
return 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 % 200 == 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.nf, 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}
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/networks.py
View File

@ -86,6 +86,10 @@ 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.StackedSwitchGenerator(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 == 'stacked_switches_5lyr':
xforms = opt_net['num_transforms'] if 'num_transforms' in opt_net.keys() else 8
netG = ssg.StackedSwitchGenerator5Layer(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_deep':
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'],