forked from mrq/DL-Art-School
More spsr3 mods
- Most branches get their own noise vector now. - First attention branch has the intended sole purpose of raw image processing - Remove norms from joiner block
This commit is contained in:
James Betker
parent
747ded2bf7
commit
df59d6c99d
|
|
@ -213,6 +213,8 @@ class ExtensibleTrainer(BaseModel):
|
|||
for v in self.opt['logger']['visuals']:
|
||||
if step % self.opt['logger']['visual_debug_rate'] == 0:
|
||||
for i, dbgv in enumerate(state[v]):
|
||||
if dbgv.shape[1] > 3:
|
||||
dbgv = dbgv[:,:3,:,:]
|
||||
os.makedirs(os.path.join(sample_save_path, v), exist_ok=True)
|
||||
utils.save_image(dbgv, os.path.join(sample_save_path, v, "%05i_%02i.png" % (step, i)))
|
||||
|
||||
|
|
|
|||
|
|
@ -356,147 +356,12 @@ class SwitchedSpsr(nn.Module):
|
|||
return val
|
||||
|
||||
|
||||
class SwitchedSpsrWithRef(nn.Module):
|
||||
def __init__(self, in_nc, out_nc, nf, xforms=8, upscale=4, init_temperature=10):
|
||||
super(SwitchedSpsrWithRef, self).__init__()
|
||||
n_upscale = int(math.log(upscale, 2))
|
||||
|
||||
# switch options
|
||||
transformation_filters = nf
|
||||
switch_filters = nf
|
||||
self.transformation_counts = xforms
|
||||
self.reference_processor = ReferenceImageBranch(transformation_filters)
|
||||
multiplx_fn = functools.partial(ReferencingConvMultiplexer, transformation_filters, switch_filters, self.transformation_counts)
|
||||
pretransform_fn = functools.partial(AdaInConvBlock, 512, transformation_filters, transformation_filters)
|
||||
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=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=3, norm=False, activation=False, bias=False)
|
||||
mplex_grad = functools.partial(ReferencingConvMultiplexer, nf * 2, nf * 2, self.transformation_counts // 2)
|
||||
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=True, 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, out_nc, kernel_size=1, norm=False, activation=False, bias=False)
|
||||
|
||||
# Conjoin branch.
|
||||
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(AdaInConvBlock, 512, transformation_filters * 2, transformation_filters * 2)
|
||||
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=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.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=False)
|
||||
self.final_hr_conv2 = 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 = init_temperature
|
||||
self.final_temperature_step = 10000
|
||||
|
||||
def forward(self, x, ref, center_coord):
|
||||
x_grad = self.get_g_nopadding(x)
|
||||
ref = self.reference_processor(ref, center_coord)
|
||||
x = self.model_fea_conv(x)
|
||||
|
||||
x1, a1 = self.sw1((x, ref), True)
|
||||
x2, a2 = self.sw2((x1, ref), 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, ref), att_in=(torch.cat([x1, x_b_fea], dim=1), ref), output_attention_weights=True)
|
||||
x_grad = self.grad_lr_conv(x_grad)
|
||||
x_grad = self.grad_hr_conv(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, ref), att_in=(x_fea, ref), identity=x_fea, output_attention_weights=True)
|
||||
x_out = self.final_lr_conv(x__branch_pretrain_cat)
|
||||
x_out = self.upsample(x_out)
|
||||
x_out = 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):
|
||||
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
|
||||
|
||||
|
||||
class MultiplexerWithReducer(nn.Module):
|
||||
def __init__(self, base_filters, multiplx_create_fn, transform_count):
|
||||
super(MultiplexerWithReducer, self).__init__()
|
||||
self.proc1 = ConvGnSilu(base_filters*2, base_filters*2, bias=False)
|
||||
self.proc2 = ConvGnSilu(base_filters*2, base_filters*2, bias=False)
|
||||
self.reduce = ConvGnSilu(base_filters*2, base_filters, activation=False, norm=False, bias=True)
|
||||
self.conjoin = ConjoinBlock(base_filters)
|
||||
self.mplex = multiplx_create_fn(transform_count)
|
||||
|
||||
def forward(self, x, ref):
|
||||
x = self.proc1(x)
|
||||
x = self.proc2(x)
|
||||
x = self.reduce(x)
|
||||
return self.mplex(x, ref)
|
||||
|
||||
|
||||
class RefJoiner(nn.Module):
|
||||
def __init__(self, nf):
|
||||
super(RefJoiner, self).__init__()
|
||||
self.lin1 = nn.Linear(512, 256)
|
||||
self.lin2 = nn.Linear(256, nf)
|
||||
self.join = ReferenceJoinBlock(nf, residual_weight_init_factor=.1, norm=False)
|
||||
self.join = ReferenceJoinBlock(nf, residual_weight_init_factor=.05, norm=False)
|
||||
|
||||
def forward(self, x, ref):
|
||||
ref = self.lin1(ref)
|
||||
|
|
@ -526,7 +391,7 @@ class SwitchedSpsrWithRef2(nn.Module):
|
|||
|
||||
# Feature branch
|
||||
self.model_fea_conv = ConvGnLelu(in_nc, nf, kernel_size=3, norm=False, activation=False)
|
||||
self.noise_ref_join = ReferenceJoinBlock(nf, residual_weight_init_factor=.1, norm=False)
|
||||
self.noise_ref_join = ReferenceJoinBlock(nf, residual_weight_init_factor=.01, norm=False)
|
||||
self.ref_join1 = RefJoiner(nf)
|
||||
self.sw1 = ConfigurableSwitchComputer(transformation_filters, multiplx_fn,
|
||||
pre_transform_block=pretransform_fn, transform_block=transform_fn,
|
||||
|
|
@ -545,6 +410,7 @@ class SwitchedSpsrWithRef2(nn.Module):
|
|||
# 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=3, norm=False, activation=False, bias=False)
|
||||
self.noise_ref_join_grad = ReferenceJoinBlock(nf, residual_weight_init_factor=.01, norm=False)
|
||||
self.ref_join3 = RefJoiner(nf)
|
||||
self.grad_ref_join = ReferenceJoinBlock(nf, residual_weight_init_factor=.2, norm=False, final_norm=False)
|
||||
self.sw_grad = ConfigurableSwitchComputer(transformation_filters, multiplx_fn,
|
||||
|
|
@ -559,6 +425,7 @@ class SwitchedSpsrWithRef2(nn.Module):
|
|||
|
||||
# Join branch (grad+fea)
|
||||
self.ref_join4 = RefJoiner(nf)
|
||||
self.noise_ref_join_conjoin = ReferenceJoinBlock(nf, residual_weight_init_factor=.01, norm=False)
|
||||
self.conjoin_ref_join = ReferenceJoinBlock(nf, residual_weight_init_factor=.2, norm=False)
|
||||
self.conjoin_sw = ConfigurableSwitchComputer(transformation_filters, multiplx_fn,
|
||||
pre_transform_block=pretransform_fn, transform_block=transform_fn,
|
||||
|
|
@ -579,16 +446,19 @@ class SwitchedSpsrWithRef2(nn.Module):
|
|||
ref = self.reference_processor(ref, center_coord)
|
||||
|
||||
x = self.model_fea_conv(x)
|
||||
x1 = self.noise_ref_join(x, torch.randn_like(x))
|
||||
x1 = x
|
||||
x1 = self.ref_join1(x1, ref)
|
||||
x1, a1 = self.sw1(x1, True, identity=x)
|
||||
|
||||
x2 = x1
|
||||
x2 = self.noise_ref_join(x2, torch.randn_like(x2))
|
||||
x2 = self.ref_join2(x2, ref)
|
||||
x2, a2 = self.sw2(x2, True, identity=x1)
|
||||
|
||||
x_grad_identity = self.grad_conv(x_grad)
|
||||
x_grad = self.ref_join3(x_grad_identity, ref)
|
||||
x_grad = self.grad_conv(x_grad)
|
||||
x_grad_identity = x_grad
|
||||
x_grad = self.noise_ref_join_grad(x_grad, torch.randn_like(x_grad))
|
||||
x_grad = self.ref_join3(x_grad, ref)
|
||||
x_grad = self.grad_ref_join(x_grad, x1)
|
||||
x_grad, a3 = self.sw_grad(x_grad, True, identity=x_grad_identity)
|
||||
x_grad = self.grad_lr_conv(x_grad)
|
||||
|
|
@ -596,7 +466,9 @@ class SwitchedSpsrWithRef2(nn.Module):
|
|||
x_grad_out = self.upsample_grad(x_grad)
|
||||
x_grad_out = self.grad_branch_output_conv(x_grad_out)
|
||||
|
||||
x_out = self.ref_join4(x2, ref)
|
||||
x_out = x2
|
||||
x_out = self.noise_ref_join_conjoin(x_out, torch.randn_like(x_out))
|
||||
x_out = self.ref_join4(x_out, ref)
|
||||
x_out = self.conjoin_ref_join(x_out, x_grad)
|
||||
x_out, a4 = self.conjoin_sw(x_out, True, identity=x2)
|
||||
x_out = self.final_lr_conv(x_out)
|
||||
|
|
|
|||
|
|
@ -456,7 +456,7 @@ class ConjoinBlock(nn.Module):
|
|||
|
||||
# Designed explicitly to join a mainline trunk with reference data. Implemented as a residual branch.
|
||||
class ReferenceJoinBlock(nn.Module):
|
||||
def __init__(self, nf, residual_weight_init_factor=1, norm=False, block=ConvGnLelu, final_norm=True):
|
||||
def __init__(self, nf, residual_weight_init_factor=1, norm=False, block=ConvGnLelu, final_norm=False):
|
||||
super(ReferenceJoinBlock, self).__init__()
|
||||
self.branch = MultiConvBlock(nf * 2, nf + nf // 2, nf, kernel_size=3, depth=3,
|
||||
scale_init=residual_weight_init_factor, norm=norm,
|
||||
|
|
|
|||
Loading…
Reference in New Issue
Block a user