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Update SSGdeep

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This commit is contained in:
James Betker 2020-10-12 10:22:08 -06:00
parent 2bc5701b10
commit ce163ad4a9
1 changed files with 18 additions and 9 deletions
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27
codes/models/archs/StructuredSwitchedGenerator.py
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@ -257,17 +257,24 @@ class StackedSwitchGenerator(SwitchModelBase):
class SSGDeep(SwitchModelBase): class SSGDeep(SwitchModelBase):
def __init__(self, in_nc, out_nc, nf, xforms=8, upscale=4, init_temperature=10): def __init__(self, in_nc, out_nc, nf, xforms=8, upscale=4, init_temperature=10, recurrent=False):
super(SSGDeep, self).__init__(init_temperature, 10000) super(SSGDeep, self).__init__(init_temperature, 10000)
n_upscale = int(math.log(upscale, 2)) n_upscale = int(math.log(upscale, 2))
self.nf = nf self.nf = nf
# processing the input embedding # processing the input embedding
if recurrent:
self.recurrent = True
self.recurrent_process = ConvGnLelu(in_nc, nf, kernel_size=3, stride=2, norm=False, bias=True, activation=False)
self.recurrent_join = ReferenceJoinBlock(nf, residual_weight_init_factor=.01, final_norm=False, kernel_size=1, depth=3, join=False)
else:
self.recurrent = False
self.reference_embedding = ReferenceImageBranch(nf) self.reference_embedding = ReferenceImageBranch(nf)
# Feature branch # Feature branch
self.model_fea_conv = ConvGnLelu(in_nc, nf, kernel_size=7, norm=False, activation=False) self.model_fea_conv = ConvGnLelu(in_nc, nf, kernel_size=7, norm=False, activation=False)
self.sw1 = SwitchWithReference(nf, xforms, init_temperature, has_ref=False) self.sw1 = SwitchWithReference(nf, xforms, init_temperature, has_ref=False)
self.sw2 = SwitchWithReference(nf, xforms, init_temperature, has_ref=False)
# Grad branch. Note - groupnorm on this branch is REALLY bad. Avoid it like the plague. # Grad branch. Note - groupnorm on this branch is REALLY bad. Avoid it like the plague.
self.get_g_nopadding = ImageGradientNoPadding() self.get_g_nopadding = ImageGradientNoPadding()
@ -279,15 +286,14 @@ class SSGDeep(SwitchModelBase):
# Join branch (grad+fea) # Join branch (grad+fea)
self.conjoin_sw = SwitchWithReference(nf, xforms, init_temperature, has_ref=True) self.conjoin_sw = SwitchWithReference(nf, xforms, init_temperature, has_ref=True)
self.sw3 = SwitchWithReference(nf, xforms, init_temperature, has_ref=False)
self.sw4 = SwitchWithReference(nf, xforms, init_temperature, has_ref=False) self.sw4 = SwitchWithReference(nf, xforms, init_temperature, has_ref=False)
self.final_lr_conv = ConvGnLelu(nf, nf, kernel_size=3, norm=False, activation=True, bias=True) 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.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_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.final_hr_conv2 = ConvGnLelu(nf // 2, out_nc, kernel_size=3, norm=False, activation=False, bias=False)
self.switches = [self.sw1.switch, self.sw_grad.switch, self.conjoin_sw.switch, self.sw3.switch, self.sw4.switch] self.switches = [self.sw1.switch, self.sw2.switch, self.sw_grad.switch, self.conjoin_sw.switch, self.sw4.switch]
def forward(self, x, ref, ref_center, save_attentions=True): def forward(self, x, ref, ref_center, save_attentions=True, recurrent=None):
# The attention_maps debugger outputs <x>. Save that here. # The attention_maps debugger outputs <x>. Save that here.
self.lr = x.detach().cpu() self.lr = x.detach().cpu()
@ -301,7 +307,11 @@ class SSGDeep(SwitchModelBase):
ref_embedding = ref_code.view(-1, ref_code.shape[1], 1, 1).repeat(1, 1, x.shape[2] // 8, x.shape[3] // 8) 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) x = self.model_fea_conv(x)
if self.recurrent:
rec = self.recurrent_process(recurrent)
x = self.recurrent_join(x, rec)
x1, a1 = checkpoint(self.sw1, x, ref_embedding) x1, a1 = checkpoint(self.sw1, x, ref_embedding)
x2, a2 = checkpoint(self.sw2, x1, ref_embedding)
x_grad = self.grad_conv(x_grad) x_grad = self.grad_conv(x_grad)
x_grad, a3, grad_fea_std = checkpoint(self.sw_grad, x_grad, ref_embedding, x1) x_grad, a3, grad_fea_std = checkpoint(self.sw_grad, x_grad, ref_embedding, x1)
@ -309,18 +319,17 @@ class SSGDeep(SwitchModelBase):
x_grad_out = checkpoint(self.upsample_grad, x_grad) x_grad_out = checkpoint(self.upsample_grad, x_grad)
x_grad_out = checkpoint(self.grad_branch_output_conv, x_grad_out) x_grad_out = checkpoint(self.grad_branch_output_conv, x_grad_out)
x_out, a4, fea_grad_std = checkpoint(self.conjoin_sw, x1, ref_embedding, x_grad) x3, a4, fea_grad_std = checkpoint(self.conjoin_sw, x2, ref_embedding, x_grad)
x_out, a5 = checkpoint(self.sw3, x_out, ref_embedding) x_out, a5 = checkpoint(self.sw4, x3, ref_embedding)
x_out, a6 = checkpoint(self.sw4, x_out, ref_embedding)
x_out = checkpoint(self.final_lr_conv, x_out) x_out = checkpoint(self.final_lr_conv, x_out)
x_out = checkpoint(self.upsample, x_out) x_out = checkpoint(self.upsample, x_out)
x_out = checkpoint(self.final_hr_conv2, x_out) x_out = checkpoint(self.final_hr_conv2, x_out)
if save_attentions: if save_attentions:
self.attentions = [a1, a3, a4, a5, a6] self.attentions = [a1, a2, a3, a4, a5]
self.grad_fea_std = grad_fea_std.detach().cpu() self.grad_fea_std = grad_fea_std.detach().cpu()
self.fea_grad_std = fea_grad_std.detach().cpu() self.fea_grad_std = fea_grad_std.detach().cpu()
return x_grad_out, x_out, x_grad return x_grad_out, x_out
class StackedSwitchGenerator5Layer(SwitchModelBase): class StackedSwitchGenerator5Layer(SwitchModelBase):