Arch cleanup
This commit is contained in:
James Betker
parent
646d6a621a
commit
7a75d10784
|
|
@ -52,150 +52,6 @@ class BasicEmbeddingPyramid(nn.Module):
|
||||||
return x, p
|
return x, p
|
||||||
|
|
||||||
|
|
||||||
class ChainedEmbeddingGen(nn.Module):
|
|
||||||
def __init__(self, depth=10, in_nc=3):
|
|
||||||
super(ChainedEmbeddingGen, self).__init__()
|
|
||||||
self.initial_conv = ConvGnLelu(in_nc, 64, kernel_size=7, bias=True, norm=False, activation=False)
|
|
||||||
self.spine = SpineNet(arch='49', output_level=[3, 4], double_reduce_early=False)
|
|
||||||
self.blocks = nn.ModuleList([BasicEmbeddingPyramid() for i in range(depth)])
|
|
||||||
self.upsample = FinalUpsampleBlock2x(64, out_nc=in_nc)
|
|
||||||
|
|
||||||
def forward(self, x):
|
|
||||||
fea = self.initial_conv(x)
|
|
||||||
emb = checkpoint(self.spine, fea)
|
|
||||||
for block in self.blocks:
|
|
||||||
fea = fea + checkpoint(block, fea, *emb)[0]
|
|
||||||
return checkpoint(self.upsample, fea),
|
|
||||||
|
|
||||||
|
|
||||||
class ChainedEmbeddingGenWithStructure(nn.Module):
|
|
||||||
def __init__(self, in_nc=3, depth=10, recurrent=False, recurrent_nf=3, recurrent_stride=2):
|
|
||||||
super(ChainedEmbeddingGenWithStructure, self).__init__()
|
|
||||||
self.recurrent = recurrent
|
|
||||||
self.initial_conv = ConvGnLelu(in_nc, 64, kernel_size=7, bias=True, norm=False, activation=False)
|
|
||||||
if recurrent:
|
|
||||||
self.recurrent_nf = recurrent_nf
|
|
||||||
self.recurrent_stride = recurrent_stride
|
|
||||||
self.recurrent_process = ConvGnLelu(recurrent_nf, 64, kernel_size=3, stride=recurrent_stride, norm=False, bias=True, activation=False)
|
|
||||||
self.recurrent_join = ReferenceJoinBlock(64, residual_weight_init_factor=.01, final_norm=False, kernel_size=1, depth=3, join=False)
|
|
||||||
self.spine = SpineNet(arch='49', output_level=[3, 4], double_reduce_early=False)
|
|
||||||
self.blocks = nn.ModuleList([BasicEmbeddingPyramid() for i in range(depth)])
|
|
||||||
self.structure_joins = nn.ModuleList([ConjoinBlock(64) for i in range(3)])
|
|
||||||
self.structure_blocks = nn.ModuleList([ConvGnLelu(64, 64, kernel_size=3, bias=False, norm=False, activation=False, weight_init_factor=.1) for i in range(3)])
|
|
||||||
self.structure_upsample = FinalUpsampleBlock2x(64)
|
|
||||||
self.grad_extract = ImageGradientNoPadding()
|
|
||||||
self.upsample = FinalUpsampleBlock2x(64)
|
|
||||||
self.ref_join_std = 0
|
|
||||||
|
|
||||||
def forward(self, x, recurrent=None):
|
|
||||||
fea = self.initial_conv(x)
|
|
||||||
if self.recurrent:
|
|
||||||
if recurrent is None:
|
|
||||||
if self.recurrent_nf == 3:
|
|
||||||
recurrent = torch.zeros_like(x)
|
|
||||||
if self.recurrent_stride != 1:
|
|
||||||
recurrent = torch.nn.functional.interpolate(recurrent, scale_factor=self.recurrent_stride, mode='nearest')
|
|
||||||
else:
|
|
||||||
recurrent = torch.zeros_like(fea)
|
|
||||||
rec = self.recurrent_process(recurrent)
|
|
||||||
fea, recstd = self.recurrent_join(fea, rec)
|
|
||||||
self.ref_join_std = recstd.item()
|
|
||||||
emb = checkpoint(self.spine, fea)
|
|
||||||
grad = fea
|
|
||||||
for i, block in enumerate(self.blocks):
|
|
||||||
fea = fea + checkpoint(block, fea, *emb)[0]
|
|
||||||
if i < 3:
|
|
||||||
structure_br = checkpoint(self.structure_joins[i], grad, fea)
|
|
||||||
grad = grad + checkpoint(self.structure_blocks[i], structure_br)
|
|
||||||
out = checkpoint(self.upsample, fea)
|
|
||||||
return out, self.grad_extract(checkpoint(self.structure_upsample, grad)), self.grad_extract(out), fea
|
|
||||||
|
|
||||||
def get_debug_values(self, step, net_name):
|
|
||||||
return { 'ref_join_std': self.ref_join_std }
|
|
||||||
|
|
||||||
|
|
||||||
# This is a structural block that learns to mute regions of a residual transformation given a signal.
|
|
||||||
class OptionalPassthroughBlock(nn.Module):
|
|
||||||
def __init__(self, nf, initial_bias=10):
|
|
||||||
super(OptionalPassthroughBlock, self).__init__()
|
|
||||||
self.switch_process = nn.Sequential(ConvGnLelu(nf, nf // 2, 1, activation=False, norm=False, bias=False),
|
|
||||||
ConvGnLelu(nf // 2, nf // 4, 1, activation=False, norm=False, bias=False),
|
|
||||||
ConvGnLelu(nf // 4, 1, 1, activation=False, norm=False, bias=False))
|
|
||||||
self.bias = nn.Parameter(torch.tensor(initial_bias, dtype=torch.float), requires_grad=True)
|
|
||||||
self.activation = nn.Sigmoid()
|
|
||||||
|
|
||||||
def forward(self, x, switch_signal):
|
|
||||||
switch = self.switch_process(switch_signal)
|
|
||||||
bypass_map = self.activation(self.bias + switch)
|
|
||||||
return x * bypass_map, bypass_map
|
|
||||||
|
|
||||||
|
|
||||||
class StructuredChainedEmbeddingGenWithBypass(nn.Module):
|
|
||||||
def __init__(self, depth=10, recurrent=False, recurrent_nf=3, recurrent_stride=2, bypass_bias=10):
|
|
||||||
super(StructuredChainedEmbeddingGenWithBypass, self).__init__()
|
|
||||||
self.recurrent = recurrent
|
|
||||||
self.initial_conv = ConvGnLelu(3, 64, kernel_size=7, bias=True, norm=False, activation=False)
|
|
||||||
if recurrent:
|
|
||||||
self.recurrent_nf = recurrent_nf
|
|
||||||
self.recurrent_stride = recurrent_stride
|
|
||||||
self.recurrent_process = ConvGnLelu(recurrent_nf, 64, kernel_size=3, stride=recurrent_stride, norm=False, bias=True, activation=False)
|
|
||||||
self.recurrent_join = ReferenceJoinBlock(64, residual_weight_init_factor=.01, final_norm=False, kernel_size=1, depth=3, join=False)
|
|
||||||
self.spine = SpineNet(arch='49', output_level=[3, 4], double_reduce_early=False)
|
|
||||||
self.blocks = nn.ModuleList([BasicEmbeddingPyramid() for i in range(depth)])
|
|
||||||
self.bypasses = nn.ModuleList([OptionalPassthroughBlock(64, initial_bias=bypass_bias) for i in range(depth)])
|
|
||||||
self.structure_joins = nn.ModuleList([ConjoinBlock(64) for i in range(3)])
|
|
||||||
self.structure_blocks = nn.ModuleList([ConvGnLelu(64, 64, kernel_size=3, bias=False, norm=False, activation=False, weight_init_factor=.1) for i in range(3)])
|
|
||||||
self.structure_upsample = FinalUpsampleBlock2x(64)
|
|
||||||
self.grad_extract = ImageGradientNoPadding()
|
|
||||||
self.upsample = FinalUpsampleBlock2x(64)
|
|
||||||
self.ref_join_std = 0
|
|
||||||
self.block_residual_means = [0 for _ in range(depth)]
|
|
||||||
self.block_residual_stds = [0 for _ in range(depth)]
|
|
||||||
self.bypass_maps = []
|
|
||||||
|
|
||||||
def forward(self, x, recurrent=None):
|
|
||||||
fea = self.initial_conv(x)
|
|
||||||
if self.recurrent:
|
|
||||||
if recurrent is None:
|
|
||||||
if self.recurrent_nf == 3:
|
|
||||||
recurrent = torch.zeros_like(x)
|
|
||||||
if self.recurrent_stride != 1:
|
|
||||||
recurrent = torch.nn.functional.interpolate(recurrent, scale_factor=self.recurrent_stride, mode='nearest')
|
|
||||||
else:
|
|
||||||
recurrent = torch.zeros_like(fea)
|
|
||||||
rec = self.recurrent_process(recurrent)
|
|
||||||
fea, recstd = self.recurrent_join(fea, rec)
|
|
||||||
self.ref_join_std = recstd.item()
|
|
||||||
emb = checkpoint(self.spine, fea)
|
|
||||||
grad = fea
|
|
||||||
self.bypass_maps = []
|
|
||||||
for i, block in enumerate(self.blocks):
|
|
||||||
residual, context = checkpoint(block, fea, *emb)
|
|
||||||
residual, bypass_map = checkpoint(self.bypasses[i], residual, context)
|
|
||||||
fea = fea + residual
|
|
||||||
self.bypass_maps.append(bypass_map.detach())
|
|
||||||
self.block_residual_means[i] = residual.mean().item()
|
|
||||||
self.block_residual_stds[i] = residual.std().item()
|
|
||||||
if i < 3:
|
|
||||||
structure_br = checkpoint(self.structure_joins[i], grad, fea)
|
|
||||||
grad = grad + checkpoint(self.structure_blocks[i], structure_br)
|
|
||||||
out = checkpoint(self.upsample, fea)
|
|
||||||
return out, self.grad_extract(checkpoint(self.structure_upsample, grad)), self.grad_extract(out), fea
|
|
||||||
|
|
||||||
def visual_dbg(self, step, path):
|
|
||||||
for i, bm in enumerate(self.bypass_maps):
|
|
||||||
torchvision.utils.save_image(bm.cpu().float(), os.path.join(path, "%i_bypass_%i.png" % (step, i+1)))
|
|
||||||
|
|
||||||
def get_debug_values(self, step, net_name):
|
|
||||||
biases = [b.bias.item() for b in self.bypasses]
|
|
||||||
blk_stds, blk_means = {}, {}
|
|
||||||
for i, (s, m) in enumerate(zip(self.block_residual_stds, self.block_residual_means)):
|
|
||||||
blk_stds['block_%i' % (i+1,)] = s
|
|
||||||
blk_means['block_%i' % (i+1,)] = m
|
|
||||||
return {'ref_join_std': self.ref_join_std, 'bypass_biases': sum(biases) / len(biases),
|
|
||||||
'blocks_std': blk_stds, 'blocks_mean': blk_means}
|
|
||||||
|
|
||||||
|
|
||||||
class MultifacetedChainedEmbeddingGen(nn.Module):
|
class MultifacetedChainedEmbeddingGen(nn.Module):
|
||||||
def __init__(self, depth=10, scale=2):
|
def __init__(self, depth=10, scale=2):
|
||||||
super(MultifacetedChainedEmbeddingGen, self).__init__()
|
super(MultifacetedChainedEmbeddingGen, self).__init__()
|
||||||
|
|
|
||||||
|
|
@ -198,263 +198,6 @@ class SPSRNetSimplified(nn.Module):
|
||||||
#########
|
#########
|
||||||
return x_out_branch, x_out, x_grad
|
return x_out_branch, x_out, x_grad
|
||||||
|
|
||||||
class Spsr5(nn.Module):
|
|
||||||
def __init__(self, in_nc, out_nc, nf, xforms=8, upscale=4, multiplexer_reductions=2, init_temperature=10):
|
|
||||||
super(Spsr5, self).__init__()
|
|
||||||
n_upscale = int(math.log(upscale, 2))
|
|
||||||
|
|
||||||
# switch options
|
|
||||||
transformation_filters = nf
|
|
||||||
self.transformation_counts = xforms
|
|
||||||
multiplx_fn = functools.partial(QueryKeyMultiplexer, transformation_filters, reductions=multiplexer_reductions)
|
|
||||||
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.noise_ref_join = ReferenceJoinBlock(nf, residual_weight_init_factor=.1)
|
|
||||||
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=False, feed_transforms_into_multiplexer=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=False, feed_transforms_into_multiplexer=True)
|
|
||||||
|
|
||||||
# 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=.1)
|
|
||||||
self.grad_ref_join = ReferenceJoinBlock(nf, residual_weight_init_factor=.3, final_norm=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 // 2, init_temp=init_temperature,
|
|
||||||
add_scalable_noise_to_transforms=False, feed_transforms_into_multiplexer=True)
|
|
||||||
self.grad_lr_conv = ConvGnLelu(nf, nf, kernel_size=3, norm=False, activation=True, bias=True)
|
|
||||||
self.grad_lr_conv2 = ConvGnLelu(nf, nf, kernel_size=3, norm=False, activation=True, bias=True)
|
|
||||||
self.upsample_grad = nn.Sequential(*[UpconvBlock(nf, nf, block=ConvGnLelu, norm=False, activation=True, bias=False) for _ in range(n_upscale)])
|
|
||||||
self.grad_branch_output_conv = ConvGnLelu(nf, out_nc, kernel_size=1, norm=False, activation=False, bias=True)
|
|
||||||
|
|
||||||
# Join branch (grad+fea)
|
|
||||||
self.noise_ref_join_conjoin = ReferenceJoinBlock(nf, residual_weight_init_factor=.1)
|
|
||||||
self.conjoin_ref_join = ReferenceJoinBlock(nf, residual_weight_init_factor=.3)
|
|
||||||
self.conjoin_sw = 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=False, feed_transforms_into_multiplexer=True)
|
|
||||||
self.final_lr_conv = ConvGnLelu(nf, nf, kernel_size=3, norm=False, activation=True, bias=True)
|
|
||||||
self.upsample = nn.Sequential(*[UpconvBlock(nf, nf, block=ConvGnLelu, norm=False, activation=True, bias=True) for _ in range(n_upscale)])
|
|
||||||
self.final_hr_conv1 = ConvGnLelu(nf, nf, kernel_size=3, norm=False, activation=False, bias=True)
|
|
||||||
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.conjoin_sw]
|
|
||||||
self.attentions = None
|
|
||||||
self.init_temperature = init_temperature
|
|
||||||
self.final_temperature_step = 10000
|
|
||||||
self.lr = None
|
|
||||||
|
|
||||||
def forward(self, x, embedding):
|
|
||||||
# The attention_maps debugger outputs <x>. Save that here.
|
|
||||||
self.lr = x.detach().cpu()
|
|
||||||
|
|
||||||
noise_stds = []
|
|
||||||
|
|
||||||
x_grad = self.get_g_nopadding(x)
|
|
||||||
|
|
||||||
x = self.model_fea_conv(x)
|
|
||||||
x1 = x
|
|
||||||
x1, a1 = self.sw1(x1, identity=x, att_in=(x1, embedding))
|
|
||||||
|
|
||||||
x2 = x1
|
|
||||||
x2, nstd = self.noise_ref_join(x2, torch.randn_like(x2))
|
|
||||||
x2, a2 = self.sw2(x2, identity=x1, att_in=(x2, embedding))
|
|
||||||
noise_stds.append(nstd)
|
|
||||||
|
|
||||||
x_grad = self.grad_conv(x_grad)
|
|
||||||
x_grad_identity = x_grad
|
|
||||||
x_grad, nstd = self.noise_ref_join_grad(x_grad, torch.randn_like(x_grad))
|
|
||||||
x_grad, grad_fea_std = self.grad_ref_join(x_grad, x1)
|
|
||||||
x_grad, a3 = self.sw_grad(x_grad, identity=x_grad_identity, att_in=(x_grad, embedding))
|
|
||||||
x_grad = self.grad_lr_conv(x_grad)
|
|
||||||
x_grad = self.grad_lr_conv2(x_grad)
|
|
||||||
x_grad_out = self.upsample_grad(x_grad)
|
|
||||||
x_grad_out = self.grad_branch_output_conv(x_grad_out)
|
|
||||||
noise_stds.append(nstd)
|
|
||||||
|
|
||||||
x_out = x2
|
|
||||||
x_out, nstd = self.noise_ref_join_conjoin(x_out, torch.randn_like(x_out))
|
|
||||||
x_out, fea_grad_std = self.conjoin_ref_join(x_out, x_grad)
|
|
||||||
x_out, a4 = self.conjoin_sw(x_out, identity=x2, att_in=(x_out, embedding))
|
|
||||||
x_out = self.final_lr_conv(x_out)
|
|
||||||
x_out = self.upsample(x_out)
|
|
||||||
x_out = self.final_hr_conv1(x_out)
|
|
||||||
x_out = self.final_hr_conv2(x_out)
|
|
||||||
noise_stds.append(nstd)
|
|
||||||
|
|
||||||
self.attentions = [a1, a2, a3, a4]
|
|
||||||
self.noise_stds = torch.stack(noise_stds).mean().detach().cpu()
|
|
||||||
self.grad_fea_std = grad_fea_std.detach().cpu()
|
|
||||||
self.fea_grad_std = fea_grad_std.detach().cpu()
|
|
||||||
return x_grad_out, 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 % 500 == 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.transformation_counts, 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,
|
|
||||||
"noise_branch_std_dev": self.noise_stds,
|
|
||||||
"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
|
|
||||||
|
|
||||||
|
|
||||||
# Variant of Spsr5 which uses multiplexer blocks that are not derived from an embedding. Also makes a few "best practices"
|
|
||||||
# adjustments learned over the past few weeks (no noise, kernel_size=7
|
|
||||||
class Spsr6(nn.Module):
|
|
||||||
def __init__(self, in_nc, out_nc, nf, xforms=8, upscale=4, multiplexer_reductions=3, init_temperature=10):
|
|
||||||
super(Spsr6, self).__init__()
|
|
||||||
n_upscale = int(math.log(upscale, 2))
|
|
||||||
|
|
||||||
# switch options
|
|
||||||
transformation_filters = nf
|
|
||||||
self.transformation_counts = xforms
|
|
||||||
multiplx_fn = functools.partial(QueryKeyPyramidMultiplexer, transformation_filters, reductions=multiplexer_reductions)
|
|
||||||
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=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)
|
|
||||||
self.sw2 = ConfigurableSwitchComputer(transformation_filters, 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)
|
|
||||||
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()
|
|
||||||
self.grad_conv = ConvGnLelu(in_nc, nf, kernel_size=3, norm=False, activation=False, bias=False)
|
|
||||||
self.grad_ref_join = ReferenceJoinBlock(nf, residual_weight_init_factor=.3, final_norm=False)
|
|
||||||
self.sw_grad = ConfigurableSwitchComputer(transformation_filters, multiplx_fn,
|
|
||||||
pre_transform_block=None, transform_block=transform_fn,
|
|
||||||
attention_norm=True,
|
|
||||||
transform_count=self.transformation_counts // 2, init_temp=init_temperature,
|
|
||||||
add_scalable_noise_to_transforms=False, feed_transforms_into_multiplexer=True)
|
|
||||||
self.grad_lr_conv = ConvGnLelu(nf, nf, kernel_size=3, norm=False, activation=True, bias=True)
|
|
||||||
self.grad_lr_conv2 = ConvGnLelu(nf, nf, kernel_size=1, norm=False, activation=True, bias=True)
|
|
||||||
self.upsample_grad = nn.Sequential(*[UpconvBlock(nf, nf, block=ConvGnLelu, norm=False, activation=True, bias=False) for _ in range(n_upscale)])
|
|
||||||
self.grad_branch_output_conv = ConvGnLelu(nf, out_nc, kernel_size=1, norm=False, activation=False, bias=True)
|
|
||||||
|
|
||||||
# Join branch (grad+fea)
|
|
||||||
self.noise_ref_join_conjoin = ReferenceJoinBlock(nf, residual_weight_init_factor=.1)
|
|
||||||
self.conjoin_ref_join = ReferenceJoinBlock(nf, residual_weight_init_factor=.3)
|
|
||||||
self.conjoin_sw = ConfigurableSwitchComputer(transformation_filters, 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)
|
|
||||||
self.final_lr_conv = ConvGnLelu(nf, nf, kernel_size=3, norm=False, activation=True, bias=True)
|
|
||||||
self.upsample = nn.Sequential(*[UpconvBlock(nf, nf, block=ConvGnLelu, norm=False, activation=True, bias=True) for _ in range(n_upscale)])
|
|
||||||
self.final_hr_conv1 = ConvGnLelu(nf, nf, kernel_size=3, norm=False, activation=False, bias=True)
|
|
||||||
self.final_hr_conv2 = ConvGnLelu(nf, out_nc, kernel_size=1, norm=False, activation=False, bias=False)
|
|
||||||
self.switches = [self.sw1, self.sw2, self.sw_grad, self.conjoin_sw]
|
|
||||||
self.attentions = None
|
|
||||||
self.init_temperature = init_temperature
|
|
||||||
self.final_temperature_step = 10000
|
|
||||||
self.lr = None
|
|
||||||
|
|
||||||
def forward(self, x):
|
|
||||||
# The attention_maps debugger outputs <x>. Save that here.
|
|
||||||
self.lr = x.detach().cpu()
|
|
||||||
|
|
||||||
x_grad = self.get_g_nopadding(x)
|
|
||||||
|
|
||||||
x = self.model_fea_conv(x)
|
|
||||||
x1 = x
|
|
||||||
x1, a1 = self.sw1(x1, identity=x)
|
|
||||||
|
|
||||||
x2 = x1
|
|
||||||
x2, a2 = self.sw2(x2, identity=x1)
|
|
||||||
|
|
||||||
x_grad = self.grad_conv(x_grad)
|
|
||||||
x_grad_identity = x_grad
|
|
||||||
x_grad, grad_fea_std = self.grad_ref_join(x_grad, x1)
|
|
||||||
x_grad, a3 = self.sw_grad(x_grad, identity=x_grad_identity)
|
|
||||||
x_grad = self.grad_lr_conv(x_grad)
|
|
||||||
x_grad = self.grad_lr_conv2(x_grad)
|
|
||||||
x_grad_out = self.upsample_grad(x_grad)
|
|
||||||
x_grad_out = self.grad_branch_output_conv(x_grad_out)
|
|
||||||
|
|
||||||
x_out = x2
|
|
||||||
x_out, fea_grad_std = self.conjoin_ref_join(x_out, x_grad)
|
|
||||||
x_out, a4 = self.conjoin_sw(x_out, identity=x2)
|
|
||||||
x_out = self.final_lr_conv(x_out)
|
|
||||||
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]
|
|
||||||
self.grad_fea_std = grad_fea_std.detach().cpu()
|
|
||||||
self.fea_grad_std = fea_grad_std.detach().cpu()
|
|
||||||
return x_grad_out, 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 % 500 == 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.transformation_counts, 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,
|
|
||||||
"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
|
|
||||||
|
|
||||||
# Variant of Spsr6 which uses multiplexer blocks that feed off of a reference embedding. Also computes that embedding.
|
# Variant of Spsr6 which uses multiplexer blocks that feed off of a reference embedding. Also computes that embedding.
|
||||||
class Spsr7(nn.Module):
|
class Spsr7(nn.Module):
|
||||||
|
|
@ -623,109 +366,6 @@ class AttentionBlock(nn.Module):
|
||||||
return self.switch(x, identity=x, att_in=(x, mplex_ref))
|
return self.switch(x, identity=x, att_in=(x, mplex_ref))
|
||||||
|
|
||||||
|
|
||||||
# SPSR7 with incremental improvements and also using the new AttentionBlock to save gpu memory.
|
|
||||||
class Spsr9(nn.Module):
|
|
||||||
def __init__(self, in_nc, out_nc, nf, xforms=8, upscale=4, multiplexer_reductions=3, init_temperature=10):
|
|
||||||
super(Spsr9, self).__init__()
|
|
||||||
n_upscale = int(math.log(upscale, 2))
|
|
||||||
self.nf = nf
|
|
||||||
self.transformation_counts = xforms
|
|
||||||
|
|
||||||
# processing the input embedding
|
|
||||||
self.reference_embedding = ReferenceImageBranch(nf)
|
|
||||||
|
|
||||||
|
|
||||||
# Feature branch
|
|
||||||
self.model_fea_conv = ConvGnLelu(in_nc, nf, kernel_size=7, norm=False, activation=False)
|
|
||||||
self.sw1 = AttentionBlock(nf, self.transformation_counts, multiplexer_reductions, init_temperature, False)
|
|
||||||
self.sw2 = AttentionBlock(nf, self.transformation_counts, multiplexer_reductions, init_temperature, False)
|
|
||||||
|
|
||||||
# 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=7, norm=False, activation=False, bias=False)
|
|
||||||
self.sw_grad = AttentionBlock(nf, self.transformation_counts // 2, multiplexer_reductions, init_temperature, True)
|
|
||||||
self.grad_lr_conv = ConvGnLelu(nf, nf, kernel_size=3, norm=False, activation=True, bias=True)
|
|
||||||
self.grad_lr_conv2 = ConvGnLelu(nf, nf, kernel_size=1, norm=False, activation=True, bias=True)
|
|
||||||
self.upsample_grad = nn.Sequential(*[UpconvBlock(nf, nf, block=ConvGnLelu, norm=False, activation=True, bias=False) for _ in range(n_upscale)])
|
|
||||||
self.grad_branch_output_conv = ConvGnLelu(nf, out_nc, kernel_size=1, norm=False, activation=False, bias=True)
|
|
||||||
|
|
||||||
# Join branch (grad+fea)
|
|
||||||
self.conjoin_sw = AttentionBlock(nf, self.transformation_counts, multiplexer_reductions, init_temperature, True)
|
|
||||||
self.final_lr_conv = ConvGnLelu(nf, nf, kernel_size=3, norm=False, activation=True, bias=True)
|
|
||||||
self.upsample = nn.Sequential(*[UpconvBlock(nf, nf, block=ConvGnLelu, norm=False, activation=True, bias=True) for _ in range(n_upscale)])
|
|
||||||
self.final_hr_conv1 = ConvGnLelu(nf, nf, kernel_size=3, norm=False, activation=False, bias=True)
|
|
||||||
self.final_hr_conv2 = ConvGnLelu(nf, out_nc, kernel_size=1, norm=False, activation=False, bias=False)
|
|
||||||
self.switches = [self.sw1.switch, self.sw2.switch, self.sw_grad.switch, self.conjoin_sw.switch]
|
|
||||||
self.attentions = None
|
|
||||||
self.init_temperature = init_temperature
|
|
||||||
self.final_temperature_step = 10000
|
|
||||||
self.lr = None
|
|
||||||
|
|
||||||
def forward(self, x, ref, ref_center, update_attention_norm=True):
|
|
||||||
# The attention_maps debugger outputs <x>. Save that here.
|
|
||||||
self.lr = x.detach().cpu()
|
|
||||||
|
|
||||||
for sw in self.switches:
|
|
||||||
sw.set_update_attention_norm(update_attention_norm)
|
|
||||||
|
|
||||||
x_grad = self.get_g_nopadding(x)
|
|
||||||
ref_code = checkpoint(self.reference_embedding, ref, ref_center)
|
|
||||||
ref_embedding = ref_code.view(-1, self.nf * 8, 1, 1).repeat(1, 1, x.shape[2] // 8, x.shape[3] // 8)
|
|
||||||
|
|
||||||
x = self.model_fea_conv(x)
|
|
||||||
x1 = x
|
|
||||||
x1, a1 = checkpoint(self.sw1, x1, ref_embedding)
|
|
||||||
x2 = x1
|
|
||||||
x2, a2 = checkpoint(self.sw2, x2, ref_embedding)
|
|
||||||
|
|
||||||
x_grad = self.grad_conv(x_grad)
|
|
||||||
x_grad_identity = x_grad
|
|
||||||
x_grad, a3, grad_fea_std = checkpoint(self.sw_grad, x_grad, ref_embedding, x1)
|
|
||||||
x_grad = self.grad_lr_conv(x_grad)
|
|
||||||
x_grad = self.grad_lr_conv2(x_grad)
|
|
||||||
x_grad_out = checkpoint(self.upsample_grad, x_grad)
|
|
||||||
x_grad_out = checkpoint(self.grad_branch_output_conv, x_grad_out)
|
|
||||||
|
|
||||||
x_out = x2
|
|
||||||
x_out, a4, fea_grad_std = checkpoint(self.conjoin_sw, x_out, ref_embedding, x_grad)
|
|
||||||
x_out = self.final_lr_conv(x_out)
|
|
||||||
x_out = checkpoint(self.upsample, x_out)
|
|
||||||
x_out = checkpoint(self.final_hr_conv1, x_out)
|
|
||||||
x_out = checkpoint(self.final_hr_conv2, x_out)
|
|
||||||
|
|
||||||
self.attentions = [a1, a2, a3, a4]
|
|
||||||
self.grad_fea_std = grad_fea_std.detach().cpu()
|
|
||||||
self.fea_grad_std = fea_grad_std.detach().cpu()
|
|
||||||
return x_grad_out, 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 % 500 == 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.transformation_counts, 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,
|
|
||||||
"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
|
|
||||||
|
|
||||||
|
|
||||||
class SwitchedSpsr(nn.Module):
|
class SwitchedSpsr(nn.Module):
|
||||||
def __init__(self, in_nc, nf, xforms=8, upscale=4, init_temperature=10):
|
def __init__(self, in_nc, nf, xforms=8, upscale=4, init_temperature=10):
|
||||||
super(SwitchedSpsr, self).__init__()
|
super(SwitchedSpsr, self).__init__()
|
||||||
|
|
|
||||||
|
|
@ -1,576 +0,0 @@
|
||||||
import functools
|
|
||||||
import math
|
|
||||||
|
|
||||||
import torch
|
|
||||||
import torch.nn.functional as F
|
|
||||||
from torch import nn
|
|
||||||
|
|
||||||
from models.archs.SPSR_arch import ImageGradientNoPadding
|
|
||||||
from models.archs.SwitchedResidualGenerator_arch import ConfigurableSwitchComputer, gather_2d, SwitchModelBase
|
|
||||||
from models.archs.arch_util import MultiConvBlock, ConvGnLelu, ConvGnSilu, ReferenceJoinBlock
|
|
||||||
from utils.util import checkpoint
|
|
||||||
|
|
||||||
|
|
||||||
# VGG-style layer with Conv(stride2)->BN->Activation->Conv->BN->Activation
|
|
||||||
# Doubles the input filter count.
|
|
||||||
class HalvingProcessingBlock(nn.Module):
|
|
||||||
def __init__(self, filters, factor=2):
|
|
||||||
super(HalvingProcessingBlock, self).__init__()
|
|
||||||
self.bnconv1 = ConvGnSilu(filters, filters, norm=False, bias=False)
|
|
||||||
self.bnconv2 = ConvGnSilu(filters, int(filters * factor), kernel_size=1, stride=2, norm=True, bias=False)
|
|
||||||
|
|
||||||
def forward(self, x):
|
|
||||||
x = self.bnconv1(x)
|
|
||||||
return self.bnconv2(x)
|
|
||||||
|
|
||||||
|
|
||||||
class ExpansionBlock2(nn.Module):
|
|
||||||
def __init__(self, filters_in, filters_out=None, block=ConvGnSilu, factor=2):
|
|
||||||
super(ExpansionBlock2, self).__init__()
|
|
||||||
if filters_out is None:
|
|
||||||
filters_out = int(filters_in / factor)
|
|
||||||
self.decimate = block(filters_in, filters_out, kernel_size=1, bias=False, activation=True, norm=False)
|
|
||||||
self.process_passthrough = block(filters_out, filters_out, kernel_size=3, bias=True, activation=True, norm=False)
|
|
||||||
self.conjoin = block(filters_out*2, filters_out*2, kernel_size=1, bias=False, activation=True, norm=False)
|
|
||||||
self.reduce = block(filters_out*2, filters_out, kernel_size=1, bias=False, activation=False, norm=True)
|
|
||||||
|
|
||||||
# input is the feature signal with shape (b, f, w, h)
|
|
||||||
# passthrough is the structure signal with shape (b, f/2, w*2, h*2)
|
|
||||||
# output is conjoined upsample with shape (b, f/2, w*2, h*2)
|
|
||||||
def forward(self, input, passthrough):
|
|
||||||
x = F.interpolate(input, scale_factor=2, mode="nearest")
|
|
||||||
x = self.decimate(x)
|
|
||||||
p = self.process_passthrough(passthrough)
|
|
||||||
x = self.conjoin(torch.cat([x, p], dim=1))
|
|
||||||
return self.reduce(x)
|
|
||||||
|
|
||||||
|
|
||||||
# Basic convolutional upsampling block that uses interpolate.
|
|
||||||
class UpconvBlock(nn.Module):
|
|
||||||
def __init__(self, filters_in, filters_out=None, block=ConvGnSilu, norm=True, activation=True, bias=False):
|
|
||||||
super(UpconvBlock, self).__init__()
|
|
||||||
self.reduce = block(filters_in, filters_out, kernel_size=1, bias=False, activation=False, norm=False)
|
|
||||||
self.process = block(filters_out, filters_out, kernel_size=3, bias=bias, activation=activation, norm=norm)
|
|
||||||
|
|
||||||
def forward(self, x):
|
|
||||||
x = self.reduce(x)
|
|
||||||
x = F.interpolate(x, scale_factor=2, mode="nearest")
|
|
||||||
return self.process(x)
|
|
||||||
|
|
||||||
|
|
||||||
class QueryKeyMultiplexer(nn.Module):
|
|
||||||
def __init__(self, nf, multiplexer_channels, embedding_channels=216, reductions=3):
|
|
||||||
super(QueryKeyMultiplexer, self).__init__()
|
|
||||||
|
|
||||||
# Blocks used to create the query
|
|
||||||
self.input_process = ConvGnSilu(nf, nf, activation=True, norm=False, bias=True)
|
|
||||||
self.embedding_process = ConvGnSilu(embedding_channels, 128, kernel_size=1, activation=True, norm=False, bias=True)
|
|
||||||
self.reduction_blocks = nn.ModuleList([HalvingProcessingBlock(int(nf * 1.5 ** i), factor=1.5) for i in range(reductions)])
|
|
||||||
reduction_filters = int(nf * 1.5 ** reductions)
|
|
||||||
self.processing_blocks = nn.Sequential(
|
|
||||||
ConvGnSilu(reduction_filters + 128, reduction_filters + 64, kernel_size=1, activation=True, norm=False, bias=True),
|
|
||||||
ConvGnSilu(reduction_filters + 64, reduction_filters, kernel_size=1, activation=True, norm=False, bias=False),
|
|
||||||
ConvGnSilu(reduction_filters, reduction_filters, kernel_size=3, activation=True, norm=True, bias=False),
|
|
||||||
ConvGnSilu(reduction_filters, reduction_filters, kernel_size=3, activation=True, norm=True, bias=False))
|
|
||||||
self.expansion_blocks = nn.ModuleList([ExpansionBlock2(int(reduction_filters // (1.5 ** i)), factor=1.5) for i in range(reductions)])
|
|
||||||
|
|
||||||
# Blocks used to create the key
|
|
||||||
self.key_process = ConvGnSilu(nf, nf, kernel_size=1, activation=True, norm=False, bias=False)
|
|
||||||
|
|
||||||
# Postprocessing blocks.
|
|
||||||
self.query_key_combine = ConvGnSilu(nf*2, nf, kernel_size=1, activation=True, norm=False, bias=False)
|
|
||||||
self.cbl1 = ConvGnSilu(nf, nf // 2, kernel_size=1, norm=True, bias=False, num_groups=4)
|
|
||||||
self.cbl2 = ConvGnSilu(nf // 2, 1, kernel_size=1, norm=False, bias=False)
|
|
||||||
|
|
||||||
def forward(self, x, embedding, transformations):
|
|
||||||
q = self.input_process(x)
|
|
||||||
embedding = self.embedding_process(embedding)
|
|
||||||
reduction_identities = []
|
|
||||||
for b in self.reduction_blocks:
|
|
||||||
reduction_identities.append(q)
|
|
||||||
q = b(q)
|
|
||||||
q = self.processing_blocks(torch.cat([q, embedding], dim=1))
|
|
||||||
for i, b in enumerate(self.expansion_blocks):
|
|
||||||
q = b(q, reduction_identities[-i - 1])
|
|
||||||
|
|
||||||
b, t, f, h, w = transformations.shape
|
|
||||||
k = transformations.view(b * t, f, h, w)
|
|
||||||
k = self.key_process(k)
|
|
||||||
|
|
||||||
q = q.view(b, 1, f, h, w).repeat(1, t, 1, 1, 1).view(b * t, f, h, w)
|
|
||||||
v = self.query_key_combine(torch.cat([q, k], dim=1))
|
|
||||||
|
|
||||||
v = self.cbl1(v)
|
|
||||||
v = self.cbl2(v)
|
|
||||||
|
|
||||||
return v.view(b, t, h, w)
|
|
||||||
|
|
||||||
|
|
||||||
# Computes a linear latent by performing processing on the reference image and returning the filters of a single point,
|
|
||||||
# which should be centered on the image patch being processed.
|
|
||||||
#
|
|
||||||
# Output is base_filters * 1.5^3.
|
|
||||||
class ReferenceImageBranch(nn.Module):
|
|
||||||
def __init__(self, base_filters=64):
|
|
||||||
super(ReferenceImageBranch, self).__init__()
|
|
||||||
final_filters = int(base_filters*1.5**3)
|
|
||||||
self.features = nn.Sequential(ConvGnSilu(4, base_filters, kernel_size=7, bias=True),
|
|
||||||
HalvingProcessingBlock(base_filters, factor=1.5),
|
|
||||||
HalvingProcessingBlock(int(base_filters*1.5), factor=1.5),
|
|
||||||
HalvingProcessingBlock(int(base_filters*1.5**2), factor=1.5),
|
|
||||||
ConvGnSilu(final_filters, final_filters, activation=True, norm=True, bias=False))
|
|
||||||
|
|
||||||
# center_point is a [b,2] long tensor describing the center point of where the patch was taken from the reference
|
|
||||||
# image.
|
|
||||||
def forward(self, x, center_point):
|
|
||||||
x = self.features(x)
|
|
||||||
return gather_2d(x, center_point // 8) # Divide by 8 to scale the center_point down.
|
|
||||||
|
|
||||||
class SwitchWithReference(nn.Module):
|
|
||||||
def __init__(self, nf, num_transforms, init_temperature=10, has_ref=True):
|
|
||||||
super(SwitchWithReference, self).__init__()
|
|
||||||
self.nf = nf
|
|
||||||
self.transformation_counts = num_transforms
|
|
||||||
multiplx_fn = functools.partial(QueryKeyMultiplexer, nf)
|
|
||||||
transform_fn = functools.partial(MultiConvBlock, nf, int(nf * 1.25), nf, kernel_size=3, depth=4, weight_init_factor=.1)
|
|
||||||
if has_ref:
|
|
||||||
self.ref_join = ReferenceJoinBlock(nf, residual_weight_init_factor=.3, final_norm=False, kernel_size=1, depth=2)
|
|
||||||
else:
|
|
||||||
self.ref_join = None
|
|
||||||
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)
|
|
||||||
|
|
||||||
def forward(self, x, mplex_ref=None, ref=None):
|
|
||||||
if self.ref_join is not None:
|
|
||||||
branch, ref_std = self.ref_join(x, ref)
|
|
||||||
return self.switch(branch, identity=x, att_in=(branch, mplex_ref)) + (ref_std,)
|
|
||||||
else:
|
|
||||||
return self.switch(x, identity=x, att_in=(x, mplex_ref))
|
|
||||||
|
|
||||||
|
|
||||||
class SSGr1(SwitchModelBase):
|
|
||||||
def __init__(self, in_nc, out_nc, nf, xforms=8, upscale=4, init_temperature=10, recurrent=False):
|
|
||||||
super(SSGr1, self).__init__(init_temperature, 10000)
|
|
||||||
n_upscale = int(math.log(upscale, 2))
|
|
||||||
self.nf = nf
|
|
||||||
|
|
||||||
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
|
|
||||||
|
|
||||||
# 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)
|
|
||||||
|
|
||||||
# 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.sw_grad = SwitchWithReference(nf, xforms // 2, init_temperature, has_ref=True)
|
|
||||||
self.grad_lr_conv = ConvGnLelu(nf, nf, kernel_size=3, norm=False, activation=True, bias=True)
|
|
||||||
self.upsample_grad = UpconvBlock(nf, nf // 2, block=ConvGnLelu, norm=False, activation=True, bias=False)
|
|
||||||
self.grad_branch_output_conv = ConvGnLelu(nf // 2, out_nc, kernel_size=1, norm=False, activation=False, bias=True)
|
|
||||||
|
|
||||||
# Join branch (grad+fea)
|
|
||||||
self.conjoin_sw = SwitchWithReference(nf, xforms, init_temperature, has_ref=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.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.switches = [self.sw1.switch, self.sw_grad.switch, self.conjoin_sw.switch]
|
|
||||||
|
|
||||||
def forward(self, x, ref, ref_center, save_attentions=True, recurrent=None):
|
|
||||||
# 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)
|
|
||||||
|
|
||||||
x_grad = self.get_g_nopadding(x)
|
|
||||||
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)
|
|
||||||
if self.recurrent:
|
|
||||||
rec = self.recurrent_process(recurrent)
|
|
||||||
x, recurrent_join_std = self.recurrent_join(x, rec)
|
|
||||||
else:
|
|
||||||
recurrent_join_std = 0
|
|
||||||
x1, a1 = checkpoint(self.sw1, x, ref_embedding)
|
|
||||||
|
|
||||||
x_grad = self.grad_conv(x_grad)
|
|
||||||
x_grad, a3, grad_fea_std = checkpoint(self.sw_grad, x_grad, ref_embedding, x1)
|
|
||||||
x_grad = checkpoint(self.grad_lr_conv, x_grad)
|
|
||||||
x_grad_out = checkpoint(self.upsample_grad, x_grad)
|
|
||||||
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)
|
|
||||||
x_out = checkpoint(self.final_lr_conv, x_out)
|
|
||||||
x_out = checkpoint(self.upsample, x_out)
|
|
||||||
x_out = checkpoint(self.final_hr_conv2, x_out)
|
|
||||||
|
|
||||||
if save_attentions:
|
|
||||||
self.attentions = [a1, a3, a4]
|
|
||||||
self.grad_fea_std = grad_fea_std.detach().cpu()
|
|
||||||
self.fea_grad_std = fea_grad_std.detach().cpu()
|
|
||||||
return x_grad_out, x_out, x_grad
|
|
||||||
|
|
||||||
|
|
||||||
class StackedSwitchGenerator(SwitchModelBase):
|
|
||||||
def __init__(self, in_nc, out_nc, nf, xforms=8, upscale=4, init_temperature=10):
|
|
||||||
super(StackedSwitchGenerator, self).__init__(init_temperature, 10000)
|
|
||||||
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, init_temperature, has_ref=False)
|
|
||||||
self.sw3 = SwitchWithReference(nf, xforms, init_temperature, has_ref=False)
|
|
||||||
self.switches = [self.sw1.switch, self.sw2.switch, self.sw3.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)
|
|
||||||
|
|
||||||
def forward(self, x, ref, ref_center, save_attentions=True):
|
|
||||||
# The attention_maps debugger outputs <x>. Save that here.
|
|
||||||
self.lr = x.detach().cpu()
|
|
||||||
|
|
||||||
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)
|
|
||||||
x_out = checkpoint(self.final_lr_conv, x3)
|
|
||||||
x_out = checkpoint(self.upsample, x_out)
|
|
||||||
x_out = checkpoint(self.final_hr_conv2, x_out)
|
|
||||||
|
|
||||||
if save_attentions:
|
|
||||||
self.attentions = [a1, a3, a3]
|
|
||||||
return x_out,
|
|
||||||
|
|
||||||
|
|
||||||
class SSGDeep(SwitchModelBase):
|
|
||||||
def __init__(self, in_nc, out_nc, nf, xforms=8, upscale=4, init_temperature=10, recurrent=False):
|
|
||||||
super(SSGDeep, self).__init__(init_temperature, 10000)
|
|
||||||
n_upscale = int(math.log(upscale, 2))
|
|
||||||
self.nf = nf
|
|
||||||
|
|
||||||
# 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)
|
|
||||||
|
|
||||||
# Feature branch
|
|
||||||
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.sw2 = SwitchWithReference(nf, xforms, init_temperature, has_ref=False)
|
|
||||||
|
|
||||||
# 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=7, norm=False, activation=False, bias=False)
|
|
||||||
self.sw_grad = SwitchWithReference(nf, xforms // 2, init_temperature, has_ref=True)
|
|
||||||
self.grad_lr_conv = ConvGnLelu(nf, nf, kernel_size=3, norm=False, activation=True, bias=True)
|
|
||||||
self.upsample_grad = UpconvBlock(nf, nf // 2, block=ConvGnLelu, norm=False, activation=True, bias=False)
|
|
||||||
self.grad_branch_output_conv = ConvGnLelu(nf // 2, out_nc, kernel_size=1, norm=False, activation=False, bias=True)
|
|
||||||
|
|
||||||
# Join branch (grad+fea)
|
|
||||||
self.conjoin_sw = SwitchWithReference(nf, xforms, init_temperature, has_ref=True)
|
|
||||||
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.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.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, recurrent=None):
|
|
||||||
# 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)
|
|
||||||
|
|
||||||
x_grad = self.get_g_nopadding(x)
|
|
||||||
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)
|
|
||||||
if self.recurrent:
|
|
||||||
rec = self.recurrent_process(recurrent)
|
|
||||||
x, recurrent_std = self.recurrent_join(x, rec)
|
|
||||||
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, a3, grad_fea_std = checkpoint(self.sw_grad, x_grad, ref_embedding, x1)
|
|
||||||
x_grad = checkpoint(self.grad_lr_conv, x_grad)
|
|
||||||
x_grad_out = checkpoint(self.upsample_grad, x_grad)
|
|
||||||
x_grad_out = checkpoint(self.grad_branch_output_conv, x_grad_out)
|
|
||||||
|
|
||||||
x3, a4, fea_grad_std = checkpoint(self.conjoin_sw, x2, ref_embedding, x_grad)
|
|
||||||
x_out, a5 = checkpoint(self.sw4, x3, ref_embedding)
|
|
||||||
x_out = checkpoint(self.final_lr_conv, x_out)
|
|
||||||
x_out = checkpoint(self.upsample, x_out)
|
|
||||||
x_out = checkpoint(self.final_hr_conv2, x_out)
|
|
||||||
|
|
||||||
if save_attentions:
|
|
||||||
self.attentions = [a1, a2, a3, a4, a5]
|
|
||||||
self.grad_fea_std = grad_fea_std.detach().cpu()
|
|
||||||
self.fea_grad_std = fea_grad_std.detach().cpu()
|
|
||||||
return x_grad_out, x_out
|
|
||||||
|
|
||||||
|
|
||||||
class StackedSwitchGenerator5Layer(SwitchModelBase):
|
|
||||||
def __init__(self, in_nc, out_nc, nf, xforms=8, upscale=4, init_temperature=10):
|
|
||||||
super(StackedSwitchGenerator5Layer, self).__init__(init_temperature, 10000)
|
|
||||||
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)
|
|
||||||
|
|
||||||
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,
|
|
||||||
|
|
||||||
|
|
||||||
class StackedSwitchGenerator2xTeco(SwitchModelBase):
|
|
||||||
def __init__(self, nf, xforms=8, init_temperature=10):
|
|
||||||
super(StackedSwitchGenerator2xTeco, self).__init__(init_temperature, 10000)
|
|
||||||
self.nf = nf
|
|
||||||
|
|
||||||
# processing the input embedding
|
|
||||||
self.reference_embedding = ReferenceImageBranch(nf)
|
|
||||||
|
|
||||||
# Feature branch
|
|
||||||
self.model_fea_conv = ConvGnLelu(3, nf, kernel_size=7, norm=False, activation=False, bias=True)
|
|
||||||
self.model_recurrent_conv = ConvGnLelu(3, nf, kernel_size=3, stride=2, norm=False, activation=False, bias=True)
|
|
||||||
self.model_fea_recurrent_combine = ConvGnLelu(nf*2, nf, 1, activation=False, norm=False, bias=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, 3, kernel_size=3, norm=False, activation=False, bias=False)
|
|
||||||
|
|
||||||
def forward(self, x, recurrent, 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)
|
|
||||||
rec = self.model_recurrent_conv(recurrent)
|
|
||||||
x = self.model_fea_recurrent_combine(torch.cat([x, rec], dim=1))
|
|
||||||
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,
|
|
||||||
|
|
||||||
|
|
||||||
class SimplePyramidMultiplexer(nn.Module):
|
|
||||||
def __init__(self, nf, transforms):
|
|
||||||
super(SimplePyramidMultiplexer, self).__init__()
|
|
||||||
|
|
||||||
# Blocks used to create the query
|
|
||||||
reductions = 3
|
|
||||||
self.input_process = ConvGnSilu(nf, nf, activation=True, norm=False, bias=True)
|
|
||||||
self.reduction_blocks = nn.ModuleList([HalvingProcessingBlock(int(nf * 1.5 ** i), factor=1.5)
|
|
||||||
for i in range(reductions)])
|
|
||||||
reduction_filters = int(nf * 1.5 ** reductions)
|
|
||||||
self.processing_blocks = nn.Sequential(
|
|
||||||
ConvGnSilu(reduction_filters, reduction_filters, kernel_size=3, activation=True, norm=True, bias=False),
|
|
||||||
ConvGnSilu(reduction_filters, reduction_filters, kernel_size=3, activation=True, norm=True, bias=False))
|
|
||||||
self.expansion_blocks = nn.ModuleList([ExpansionBlock2(int(reduction_filters // (1.5 ** i)), factor=1.5)
|
|
||||||
for i in range(reductions)])
|
|
||||||
|
|
||||||
self.cbl1 = ConvGnSilu(nf, nf // 2, kernel_size=1, norm=False, bias=False)
|
|
||||||
self.cbl2 = ConvGnSilu(nf // 2, transforms, kernel_size=1, norm=False, bias=False)
|
|
||||||
|
|
||||||
def forward(self, x):
|
|
||||||
q = self.input_process(x)
|
|
||||||
reduction_identities = []
|
|
||||||
for b in self.reduction_blocks:
|
|
||||||
reduction_identities.append(q)
|
|
||||||
q = b(q)
|
|
||||||
q = self.processing_blocks(q)
|
|
||||||
for i, b in enumerate(self.expansion_blocks):
|
|
||||||
q = b(q, reduction_identities[-i - 1])
|
|
||||||
q = self.cbl1(q)
|
|
||||||
q = self.cbl2(q)
|
|
||||||
return q
|
|
||||||
|
|
||||||
|
|
||||||
class SimplerSwitchWithReference(nn.Module):
|
|
||||||
def __init__(self, nf, num_transforms, init_temperature=10, has_ref=True):
|
|
||||||
super(SimplerSwitchWithReference, self).__init__()
|
|
||||||
self.nf = nf
|
|
||||||
self.transformation_counts = num_transforms
|
|
||||||
multiplx_fn = functools.partial(SimplePyramidMultiplexer, nf)
|
|
||||||
pretransform = functools.partial(ConvGnLelu, nf, int(nf*1.5), kernel_size=3, bias=False, norm=False, activation=True, weight_init_factor=.1)
|
|
||||||
transform_fn = functools.partial(ConvGnLelu, int(nf * 1.5), int(nf * 1.5), kernel_size=3, bias=False, norm=False, activation=True, weight_init_factor=.1)
|
|
||||||
posttransform = ConvGnLelu(int(nf*1.5), nf, kernel_size=3, bias=False, norm=False, activation=True, weight_init_factor=.1)
|
|
||||||
if has_ref:
|
|
||||||
self.ref_join = ReferenceJoinBlock(nf, residual_weight_init_factor=.3, final_norm=False, kernel_size=1, depth=2)
|
|
||||||
else:
|
|
||||||
self.ref_join = None
|
|
||||||
self.switch = ConfigurableSwitchComputer(nf, multiplx_fn,
|
|
||||||
pre_transform_block=pretransform, transform_block=transform_fn,
|
|
||||||
post_transform_block=posttransform,
|
|
||||||
attention_norm=True,
|
|
||||||
transform_count=self.transformation_counts, init_temp=init_temperature,
|
|
||||||
add_scalable_noise_to_transforms=False, feed_transforms_into_multiplexer=False)
|
|
||||||
|
|
||||||
def forward(self, x, ref=None):
|
|
||||||
if self.ref_join is not None:
|
|
||||||
branch, ref_std = self.ref_join(x, ref)
|
|
||||||
return self.switch(branch, identity=x) + (ref_std,)
|
|
||||||
else:
|
|
||||||
return self.switch(x, identity=x)
|
|
||||||
|
|
||||||
|
|
||||||
class SsgSimpler(SwitchModelBase):
|
|
||||||
def __init__(self, in_nc, out_nc, nf, xforms=8, init_temperature=10, recurrent=False):
|
|
||||||
super(SsgSimpler, self).__init__(init_temperature, 10000)
|
|
||||||
self.nf = nf
|
|
||||||
|
|
||||||
# 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
|
|
||||||
|
|
||||||
# Feature branch
|
|
||||||
self.model_fea_conv = ConvGnLelu(in_nc, nf, kernel_size=7, norm=False, activation=False)
|
|
||||||
self.sw1 = SimplerSwitchWithReference(nf, xforms, init_temperature, has_ref=False)
|
|
||||||
self.sw2 = SimplerSwitchWithReference(nf, xforms, init_temperature, has_ref=False)
|
|
||||||
|
|
||||||
# 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=7, norm=False, activation=False, bias=False)
|
|
||||||
self.sw_grad = SimplerSwitchWithReference(nf, xforms // 2, init_temperature, has_ref=True)
|
|
||||||
self.grad_lr_conv = ConvGnLelu(nf, nf, kernel_size=3, norm=False, activation=True, bias=True)
|
|
||||||
self.upsample_grad = UpconvBlock(nf, nf // 2, block=ConvGnLelu, norm=False, activation=True, bias=False)
|
|
||||||
self.grad_branch_output_conv = ConvGnLelu(nf // 2, out_nc, kernel_size=1, norm=False, activation=False, bias=True)
|
|
||||||
|
|
||||||
# Join branch (grad+fea)
|
|
||||||
self.conjoin_sw = SimplerSwitchWithReference(nf, xforms, init_temperature, has_ref=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.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.switches = [self.sw1.switch, self.sw2.switch, self.sw_grad.switch, self.conjoin_sw.switch]
|
|
||||||
|
|
||||||
def forward(self, x, save_attentions=True, recurrent=None):
|
|
||||||
# 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)
|
|
||||||
|
|
||||||
x1 = self.model_fea_conv(x)
|
|
||||||
if self.recurrent:
|
|
||||||
rec = self.recurrent_process(recurrent)
|
|
||||||
x1, recurrent_std = self.recurrent_join(x1, rec)
|
|
||||||
x1, a1 = checkpoint(self.sw1, x1)
|
|
||||||
x2, a2 = checkpoint(self.sw2, x1)
|
|
||||||
|
|
||||||
x_grad = self.get_g_nopadding(x)
|
|
||||||
x_grad = self.grad_conv(x_grad)
|
|
||||||
x_grad, a3, grad_fea_std = checkpoint(self.sw_grad, x_grad, x1)
|
|
||||||
x_grad = checkpoint(self.grad_lr_conv, x_grad)
|
|
||||||
x_grad_out = checkpoint(self.upsample_grad, x_grad)
|
|
||||||
x_grad_out = checkpoint(self.grad_branch_output_conv, x_grad_out)
|
|
||||||
|
|
||||||
x3, a4, fea_grad_std = checkpoint(self.conjoin_sw, x2, x_grad)
|
|
||||||
x_out = checkpoint(self.final_lr_conv, x3)
|
|
||||||
x_out = checkpoint(self.upsample, x_out)
|
|
||||||
x_out = checkpoint(self.final_hr_conv2, x_out)
|
|
||||||
|
|
||||||
if save_attentions:
|
|
||||||
self.attentions = [a1, a2, a3, a4]
|
|
||||||
self.grad_fea_std = grad_fea_std.detach().cpu()
|
|
||||||
self.fea_grad_std = fea_grad_std.detach().cpu()
|
|
||||||
return x_grad_out, x_out
|
|
||||||
|
|
@ -614,131 +614,6 @@ class SwitchModelBase(nn.Module):
|
||||||
return val
|
return val
|
||||||
|
|
||||||
|
|
||||||
from models.archs.spinenet_arch import make_res_layer, BasicBlock
|
|
||||||
class BigMultiplexer(nn.Module):
|
|
||||||
def __init__(self, in_nc, nf, multiplexer_channels):
|
|
||||||
super(BigMultiplexer, 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 = nn.Sequential(ConvGnSilu(nf, nf // 2, kernel_size=1, activation=True, norm=True, bias=False),
|
|
||||||
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)
|
|
||||||
return self.reduce(q)
|
|
||||||
|
|
||||||
|
|
||||||
class TheBigSwitch(SwitchModelBase):
|
|
||||||
def __init__(self, in_nc, nf, xforms=16, upscale=2, init_temperature=10):
|
|
||||||
super(TheBigSwitch, self).__init__(init_temperature, 10000)
|
|
||||||
self.nf = nf
|
|
||||||
self.transformation_counts = xforms
|
|
||||||
|
|
||||||
self.model_fea_conv = ConvGnLelu(in_nc, nf, kernel_size=7, norm=False, activation=False)
|
|
||||||
|
|
||||||
multiplx_fn = functools.partial(BigMultiplexer, in_nc, nf)
|
|
||||||
transform_fn = functools.partial(MultiConvBlock, nf, int(nf * 1.5), nf, 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)
|
|
||||||
self.switches = [self.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, 3, kernel_size=3, norm=False, activation=False, bias=False)
|
|
||||||
|
|
||||||
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)
|
|
||||||
|
|
||||||
x1 = self.model_fea_conv(x)
|
|
||||||
x1, a1 = self.switch(x1, att_in=x, do_checkpointing=True)
|
|
||||||
x_out = checkpoint(self.final_lr_conv, x1)
|
|
||||||
x_out = checkpoint(self.upsample, x_out)
|
|
||||||
x_out = checkpoint(self.final_hr_conv2, x_out)
|
|
||||||
|
|
||||||
if save_attentions:
|
|
||||||
self.attentions = [a1]
|
|
||||||
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__':
|
if __name__ == '__main__':
|
||||||
tbs = TheBigSwitch(3, 64)
|
tbs = TheBigSwitch(3, 64)
|
||||||
x = torch.randn(4,3,64,64)
|
x = torch.randn(4,3,64,64)
|
||||||
|
|
|
||||||
|
|
@ -12,14 +12,12 @@ import models.archs.DiscriminatorResnet_arch_passthrough as DiscriminatorResnet_
|
||||||
import models.archs.RRDBNet_arch as RRDBNet_arch
|
import models.archs.RRDBNet_arch as RRDBNet_arch
|
||||||
import models.archs.SPSR_arch as spsr
|
import models.archs.SPSR_arch as spsr
|
||||||
import models.archs.SRResNet_arch as SRResNet_arch
|
import models.archs.SRResNet_arch as SRResNet_arch
|
||||||
import models.archs.StructuredSwitchedGenerator as ssg
|
|
||||||
import models.archs.SwitchedResidualGenerator_arch as SwitchedGen_arch
|
import models.archs.SwitchedResidualGenerator_arch as SwitchedGen_arch
|
||||||
import models.archs.discriminator_vgg_arch as SRGAN_arch
|
import models.archs.discriminator_vgg_arch as SRGAN_arch
|
||||||
import models.archs.feature_arch as feature_arch
|
import models.archs.feature_arch as feature_arch
|
||||||
import models.archs.panet.panet as panet
|
import models.archs.panet.panet as panet
|
||||||
import models.archs.rcan as rcan
|
import models.archs.rcan as rcan
|
||||||
from models.archs.ChainedEmbeddingGen import ChainedEmbeddingGen, ChainedEmbeddingGenWithStructure, \
|
import models.archs.ChainedEmbeddingGen as chained
|
||||||
StructuredChainedEmbeddingGenWithBypass, MultifacetedChainedEmbeddingGen
|
|
||||||
|
|
||||||
logger = logging.getLogger('base')
|
logger = logging.getLogger('base')
|
||||||
|
|
||||||
|
|
@ -72,76 +70,15 @@ def define_G(opt, net_key='network_G', scale=None):
|
||||||
nb=opt_net['nb'], upscale=opt_net['scale'])
|
nb=opt_net['nb'], upscale=opt_net['scale'])
|
||||||
elif which_model == "spsr_switched":
|
elif which_model == "spsr_switched":
|
||||||
netG = spsr.SwitchedSpsr(in_nc=3, nf=opt_net['nf'], upscale=opt_net['scale'], init_temperature=opt_net['temperature'])
|
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'],
|
|
||||||
multiplexer_reductions=opt_net['multiplexer_reductions'] if 'multiplexer_reductions' in opt_net.keys() else 2,
|
|
||||||
init_temperature=opt_net['temperature'] if 'temperature' in opt_net.keys() else 10)
|
|
||||||
elif which_model == "spsr6":
|
|
||||||
xforms = opt_net['num_transforms'] if 'num_transforms' in opt_net.keys() else 8
|
|
||||||
netG = spsr.Spsr6(in_nc=3, out_nc=3, nf=opt_net['nf'], xforms=xforms, upscale=opt_net['scale'],
|
|
||||||
multiplexer_reductions=opt_net['multiplexer_reductions'] if 'multiplexer_reductions' in opt_net.keys() else 3,
|
|
||||||
init_temperature=opt_net['temperature'] if 'temperature' in opt_net.keys() else 10)
|
|
||||||
elif which_model == "spsr7":
|
elif which_model == "spsr7":
|
||||||
recurrent = opt_net['recurrent'] if 'recurrent' in opt_net.keys() else False
|
recurrent = opt_net['recurrent'] if 'recurrent' in opt_net.keys() else False
|
||||||
xforms = opt_net['num_transforms'] if 'num_transforms' in opt_net.keys() else 8
|
xforms = opt_net['num_transforms'] if 'num_transforms' in opt_net.keys() else 8
|
||||||
netG = spsr.Spsr7(in_nc=3, out_nc=3, nf=opt_net['nf'], xforms=xforms, upscale=opt_net['scale'],
|
netG = spsr.Spsr7(in_nc=3, out_nc=3, nf=opt_net['nf'], xforms=xforms, upscale=opt_net['scale'],
|
||||||
multiplexer_reductions=opt_net['multiplexer_reductions'] if 'multiplexer_reductions' in opt_net.keys() else 3,
|
multiplexer_reductions=opt_net['multiplexer_reductions'] if 'multiplexer_reductions' in opt_net.keys() else 3,
|
||||||
init_temperature=opt_net['temperature'] if 'temperature' in opt_net.keys() else 10, recurrent=recurrent)
|
init_temperature=opt_net['temperature'] if 'temperature' in opt_net.keys() else 10, recurrent=recurrent)
|
||||||
elif which_model == "spsr9":
|
|
||||||
xforms = opt_net['num_transforms'] if 'num_transforms' in opt_net.keys() else 8
|
|
||||||
netG = spsr.Spsr9(in_nc=3, out_nc=3, nf=opt_net['nf'], xforms=xforms, upscale=opt_net['scale'],
|
|
||||||
multiplexer_reductions=opt_net['multiplexer_reductions'] if 'multiplexer_reductions' in opt_net.keys() else 3,
|
|
||||||
init_temperature=opt_net['temperature'] if 'temperature' in opt_net.keys() else 10)
|
|
||||||
elif which_model == "ssgr1":
|
|
||||||
recurrent = opt_net['recurrent'] if 'recurrent' in opt_net.keys() else False
|
|
||||||
xforms = opt_net['num_transforms'] if 'num_transforms' in opt_net.keys() else 8
|
|
||||||
netG = ssg.SSGr1(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, recurrent=recurrent)
|
|
||||||
elif which_model == 'stacked_switches':
|
|
||||||
xforms = opt_net['num_transforms'] if 'num_transforms' in opt_net.keys() else 8
|
|
||||||
in_nc = opt_net['in_nc'] if 'in_nc' in opt_net.keys() else 3
|
|
||||||
netG = ssg.StackedSwitchGenerator(in_nc=in_nc, 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
|
|
||||||
in_nc = opt_net['in_nc'] if 'in_nc' in opt_net.keys() else 3
|
|
||||||
netG = ssg.StackedSwitchGenerator5Layer(in_nc=in_nc, 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'],
|
|
||||||
init_temperature=opt_net['temperature'] if 'temperature' in opt_net.keys() else 10)
|
|
||||||
elif which_model == 'ssg_simpler':
|
|
||||||
xforms = opt_net['num_transforms'] if 'num_transforms' in opt_net.keys() else 8
|
|
||||||
netG = ssg.SsgSimpler(in_nc=3, out_nc=3, nf=opt_net['nf'], xforms=xforms,
|
|
||||||
init_temperature=opt_net['temperature'] if 'temperature' in opt_net.keys() else 10)
|
|
||||||
elif which_model == 'ssg_teco':
|
|
||||||
netG = ssg.StackedSwitchGenerator2xTeco(nf=opt_net['nf'], xforms=opt_net['num_transforms'], init_temperature=opt_net['temperature'] if 'temperature' in opt_net.keys() else 10)
|
|
||||||
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 == 'chained_gen':
|
|
||||||
in_nc = opt_net['in_nc'] if 'in_nc' in opt_net.keys() else 3
|
|
||||||
netG = ChainedEmbeddingGen(depth=opt_net['depth'], in_nc=in_nc)
|
|
||||||
elif which_model == 'chained_gen_structured':
|
|
||||||
rec = opt_net['recurrent'] if 'recurrent' in opt_net.keys() else False
|
|
||||||
recnf = opt_net['recurrent_nf'] if 'recurrent_nf' in opt_net.keys() else 3
|
|
||||||
recstd = opt_net['recurrent_stride'] if 'recurrent_stride' in opt_net.keys() else 2
|
|
||||||
in_nc = opt_net['in_nc'] if 'in_nc' in opt_net.keys() else 3
|
|
||||||
netG = ChainedEmbeddingGenWithStructure(depth=opt_net['depth'], recurrent=rec, recurrent_nf=recnf, recurrent_stride=recstd, in_nc=in_nc)
|
|
||||||
elif which_model == 'chained_gen_structured_with_bypass':
|
|
||||||
rec = opt_net['recurrent'] if 'recurrent' in opt_net.keys() else False
|
|
||||||
recnf = opt_net['recurrent_nf'] if 'recurrent_nf' in opt_net.keys() else 3
|
|
||||||
recstd = opt_net['recurrent_stride'] if 'recurrent_stride' in opt_net.keys() else 2
|
|
||||||
bypass_bias = opt_net['bypass_bias'] if 'bypass_bias' in opt_net.keys() else 0
|
|
||||||
netG = StructuredChainedEmbeddingGenWithBypass(depth=opt_net['depth'], recurrent=rec, recurrent_nf=recnf, recurrent_stride=recstd, bypass_bias=bypass_bias)
|
|
||||||
elif which_model == 'multifaceted_chained':
|
elif which_model == 'multifaceted_chained':
|
||||||
scale = opt_net['scale'] if 'scale' in opt_net.keys() else 2
|
scale = opt_net['scale'] if 'scale' in opt_net.keys() else 2
|
||||||
netG = MultifacetedChainedEmbeddingGen(depth=opt_net['depth'], scale=scale)
|
netG = chained.MultifacetedChainedEmbeddingGen(depth=opt_net['depth'], scale=scale)
|
||||||
elif which_model == "flownet2":
|
elif which_model == "flownet2":
|
||||||
from models.flownet2.models import FlowNet2
|
from models.flownet2.models import FlowNet2
|
||||||
ld = torch.load(opt_net['load_path'])
|
ld = torch.load(opt_net['load_path'])
|
||||||
|
|
|
||||||
Loading…
Reference in New Issue
Block a user