import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from models.archs import SPSR_util as B
from .RRDBNet_arch import RRDB
from models.archs.arch_util import ConvGnLelu, UpconvBlock, ConjoinBlock, ConvGnSilu, MultiConvBlock, ReferenceJoinBlock
from models.archs.SwitchedResidualGenerator_arch import ConvBasisMultiplexer, ConfigurableSwitchComputer, ReferencingConvMultiplexer, ReferenceImageBranch, AdaInConvBlock, ProcessingBranchWithStochasticity, EmbeddingMultiplexer, QueryKeyMultiplexer
from switched_conv_util import save_attention_to_image_rgb
from switched_conv import compute_attention_specificity
import functools
import os
class ImageGradient(nn.Module):
def __init__(self):
super(ImageGradient, self).__init__()
kernel_v = [[0, -1, 0],
[0, 0, 0],
[0, 1, 0]]
kernel_h = [[0, 0, 0],
[-1, 0, 1],
[0, 0, 0]]
kernel_h = torch.FloatTensor(kernel_h).unsqueeze(0).unsqueeze(0)
kernel_v = torch.FloatTensor(kernel_v).unsqueeze(0).unsqueeze(0)
self.weight_h = nn.Parameter(data = kernel_h, requires_grad = False).cuda()
self.weight_v = nn.Parameter(data = kernel_v, requires_grad = False).cuda()
def forward(self, x):
x0 = x[:, 0]
x1 = x[:, 1]
x2 = x[:, 2]
x0_v = F.conv2d(x0.unsqueeze(1), self.weight_v, padding=2)
x0_h = F.conv2d(x0.unsqueeze(1), self.weight_h, padding=2)
x1_v = F.conv2d(x1.unsqueeze(1), self.weight_v, padding=2)
x1_h = F.conv2d(x1.unsqueeze(1), self.weight_h, padding=2)
x2_v = F.conv2d(x2.unsqueeze(1), self.weight_v, padding=2)
x2_h = F.conv2d(x2.unsqueeze(1), self.weight_h, padding=2)
x0 = torch.sqrt(torch.pow(x0_v, 2) + torch.pow(x0_h, 2) + 1e-6)
x1 = torch.sqrt(torch.pow(x1_v, 2) + torch.pow(x1_h, 2) + 1e-6)
x2 = torch.sqrt(torch.pow(x2_v, 2) + torch.pow(x2_h, 2) + 1e-6)
x = torch.cat([x0, x1, x2], dim=1)
return x
class ImageGradientNoPadding(nn.Module):
def __init__(self):
super(ImageGradientNoPadding, self).__init__()
kernel_v = [[0, -1, 0],
[0, 0, 0],
[0, 1, 0]]
kernel_h = [[0, 0, 0],
[-1, 0, 1],
[0, 0, 0]]
kernel_h = torch.FloatTensor(kernel_h).unsqueeze(0).unsqueeze(0)
kernel_v = torch.FloatTensor(kernel_v).unsqueeze(0).unsqueeze(0)
self.weight_h = nn.Parameter(data = kernel_h, requires_grad = False)
self.weight_v = nn.Parameter(data = kernel_v, requires_grad = False)
def forward(self, x):
x_list = []
for i in range(x.shape[1]):
x_i = x[:, i]
x_i_v = F.conv2d(x_i.unsqueeze(1), self.weight_v, padding=1)
x_i_h = F.conv2d(x_i.unsqueeze(1), self.weight_h, padding=1)
x_i = torch.sqrt(torch.pow(x_i_v, 2) + torch.pow(x_i_h, 2) + 1e-6)
x_list.append(x_i)
x = torch.cat(x_list, dim = 1)
return x
####################
# Generator
####################
class SPSRNet(nn.Module):
def __init__(self, in_nc, out_nc, nf, nb, gc=32, upscale=4, norm_type=None, \
act_type='leakyrelu', mode='CNA', upsample_mode='upconv', bl_inc=5):
super(SPSRNet, self).__init__()
self.bl_inc = bl_inc
n_upscale = int(math.log(upscale, 2))
if upscale == 3:
n_upscale = 1
fea_conv = B.conv_block(in_nc + 1, nf, kernel_size=3, norm_type=None, act_type=None)
rb_blocks = [RRDB(nf, gc=32) for _ in range(nb)]
LR_conv = B.conv_block(nf, nf, kernel_size=3, norm_type=norm_type, act_type=None, mode=mode)
if upsample_mode == 'upconv':
upsample_block = B.upconv_block
elif upsample_mode == 'pixelshuffle':
upsample_block = B.pixelshuffle_block
else:
raise NotImplementedError('upsample mode [{:s}] is not found'.format(upsample_mode))
if upscale == 3:
upsampler = upsample_block(nf, nf, 3, act_type=act_type)
else:
upsampler = [upsample_block(nf, nf, act_type=act_type) for _ in range(n_upscale)]
self.HR_conv0_new = B.conv_block(nf, nf, kernel_size=3, norm_type=None, act_type=act_type)
self.HR_conv1_new = B.conv_block(nf, nf, kernel_size=3, norm_type=None, act_type=None)
self.model = B.sequential(fea_conv, B.ShortcutBlock(B.sequential(*rb_blocks, LR_conv)),\
*upsampler, self.HR_conv0_new)
self.get_g_nopadding = ImageGradientNoPadding()
self.b_fea_conv = B.conv_block(in_nc, nf, kernel_size=3, norm_type=None, act_type=None)
self.b_concat_1 = B.conv_block(2*nf, nf, kernel_size=3, norm_type=None, act_type = None)
self.b_block_1 = RRDB(nf*2, gc=32)
self.b_concat_2 = B.conv_block(2*nf, nf, kernel_size=3, norm_type=None, act_type = None)
self.b_block_2 = RRDB(nf*2, gc=32)
self.b_concat_3 = B.conv_block(2*nf, nf, kernel_size=3, norm_type=None, act_type = None)
self.b_block_3 = RRDB(nf*2, gc=32)
self.b_concat_4 = B.conv_block(2*nf, nf, kernel_size=3, norm_type=None, act_type = None)
self.b_block_4 = RRDB(nf*2, gc=32)
self.b_LR_conv = B.conv_block(nf, nf, kernel_size=3, norm_type=norm_type, act_type=None, mode=mode)
if upsample_mode == 'upconv':
upsample_block = B.upconv_block
elif upsample_mode == 'pixelshuffle':
upsample_block = B.pixelshuffle_block
else:
raise NotImplementedError('upsample mode [{:s}] is not found'.format(upsample_mode))
if upscale == 3:
b_upsampler = upsample_block(nf, nf, 3, act_type=act_type)
else:
b_upsampler = [upsample_block(nf, nf, act_type=act_type) for _ in range(n_upscale)]
b_HR_conv0 = B.conv_block(nf, nf, kernel_size=3, norm_type=None, act_type=act_type)
b_HR_conv1 = B.conv_block(nf, nf, kernel_size=3, norm_type=None, act_type=None)
self.b_module = B.sequential(*b_upsampler, b_HR_conv0, b_HR_conv1)
self.conv_w = B.conv_block(nf, out_nc, kernel_size=1, norm_type=None, act_type=None)
# Note: "_branch_pretrain" is a special tag used to denote parameters that get pretrained before the rest.
self._branch_pretrain_concat = B.conv_block(nf*2, nf, kernel_size=3, norm_type=None, act_type=None)
self._branch_pretrain_block = RRDB(nf*2, gc=32)
self._branch_pretrain_HR_conv0 = B.conv_block(nf, nf, kernel_size=3, norm_type=None, act_type=act_type)
self._branch_pretrain_HR_conv1 = B.conv_block(nf, out_nc, kernel_size=3, norm_type=None, act_type=None)
def forward(self, x: torch.Tensor):
x_grad = self.get_g_nopadding(x)
b, f, w, h = x.shape
x = torch.cat([x, torch.randn(b, 1, w, h, device=x.get_device())], dim=1)
x = self.model[0](x)
x, block_list = self.model[1](x)
x_ori = x
for i in range(self.bl_inc):
x = block_list[i](x)
x_fea1 = x
for i in range(self.bl_inc):
x = block_list[i+self.bl_inc](x)
x_fea2 = x
for i in range(self.bl_inc):
x = block_list[i+self.bl_inc*2](x)
x_fea3 = x
for i in range(self.bl_inc):
x = block_list[i+self.bl_inc*3](x)
x_fea4 = x
x = block_list[self.bl_inc*4:](x)
#short cut
x = x_ori+x
x= self.model[2:](x)
x = self.HR_conv1_new(x)
x_b_fea = self.b_fea_conv(x_grad)
x_cat_1 = torch.cat([x_b_fea, x_fea1], dim=1)
x_cat_1 = self.b_block_1(x_cat_1)
x_cat_1 = self.b_concat_1(x_cat_1)
x_cat_2 = torch.cat([x_cat_1, x_fea2], dim=1)
x_cat_2 = self.b_block_2(x_cat_2)
x_cat_2 = self.b_concat_2(x_cat_2)
x_cat_3 = torch.cat([x_cat_2, x_fea3], dim=1)
x_cat_3 = self.b_block_3(x_cat_3)
x_cat_3 = self.b_concat_3(x_cat_3)
x_cat_4 = torch.cat([x_cat_3, x_fea4], dim=1)
x_cat_4 = self.b_block_4(x_cat_4)
x_cat_4 = self.b_concat_4(x_cat_4)
x_cat_4 = self.b_LR_conv(x_cat_4)
#short cut
x_cat_4 = x_cat_4+x_b_fea
x_branch = self.b_module(x_cat_4)
x_out_branch = self.conv_w(x_branch)
########
x_branch_d = x_branch
x__branch_pretrain_cat = torch.cat([x_branch_d, x], dim=1)
x__branch_pretrain_cat = self._branch_pretrain_block(x__branch_pretrain_cat)
x_out = self._branch_pretrain_concat(x__branch_pretrain_cat)
x_out = self._branch_pretrain_HR_conv0(x_out)
x_out = self._branch_pretrain_HR_conv1(x_out)
#########
return x_out_branch, x_out, x_grad
class RefJoiner(nn.Module):
def __init__(self, nf):
super(RefJoiner, self).__init__()
self.lin1 = nn.Linear(nf * 8, nf * 4)
self.lin2 = nn.Linear(nf * 4, nf)
self.join = ReferenceJoinBlock(nf, residual_weight_init_factor=.1)
def forward(self, x, ref):
ref = self.lin1(ref)
ref = self.lin2(ref)
b, _, h, w = x.shape
ref = ref.view(b, -1, 1, 1)
return self.join(x, ref.repeat((1, 1, h, w)))
class ModuleWithRef(nn.Module):
def __init__(self, nf, mcnv, *args):
super(ModuleWithRef, self).__init__()
self.join = ReferenceJoinBlock(nf, residual_weight_init_factor=.2)
self.multi = mcnv(*args)
def forward(self, x, ref):
out, _ = self.join(x, ref)
return self.multi(out)
class SwitchedSpsrWithRef2(nn.Module):
def __init__(self, in_nc, out_nc, nf, xforms=8, upscale=4, init_temperature=10):
super(SwitchedSpsrWithRef2, self).__init__()
n_upscale = int(math.log(upscale, 2))
# switch options
transformation_filters = nf
switch_filters = nf
self.transformation_counts = xforms
multiplx_fn = functools.partial(ConvBasisMultiplexer, transformation_filters, switch_filters, 3,
2, use_exp2=True)
pretransform_fn = functools.partial(ConvGnLelu, transformation_filters, transformation_filters, norm=False, bias=False, weight_init_factor=.1)
transform_fn = functools.partial(MultiConvBlock, transformation_filters, int(transformation_filters * 1.5),
transformation_filters, kernel_size=3, depth=3,
weight_init_factor=.1)
self.reference_processor = ReferenceImageBranch(transformation_filters)
# 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.ref_join1 = RefJoiner(nf)
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)
self.ref_join2 = RefJoiner(nf)
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)
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.noise_ref_join_grad = ReferenceJoinBlock(nf, residual_weight_init_factor=.1)
self.ref_join3 = RefJoiner(nf)
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)
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.ref_join4 = RefJoiner(nf)
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)
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
def forward(self, x, ref, center_coord):
ref_stds = []
noise_stds = []
x_grad = self.get_g_nopadding(x)
ref = self.reference_processor(ref, center_coord)
x = self.model_fea_conv(x)
x1 = x
x1, rstd = self.ref_join1(x1, ref)
x1, a1 = self.sw1(x1, True, identity=x)
ref_stds.append(rstd)
x2 = x1
x2, nstd = self.noise_ref_join(x2, torch.randn_like(x2))
x2, rstd = self.ref_join2(x2, ref)
x2, a2 = self.sw2(x2, True, identity=x1)
noise_stds.append(nstd)
ref_stds.append(rstd)
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, rstd = self.ref_join3(x_grad, ref)
x_grad, grad_fea_std = 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)
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)
ref_stds.append(rstd)
x_out = x2
x_out, nstd = self.noise_ref_join_conjoin(x_out, torch.randn_like(x_out))
x_out, rstd = self.ref_join4(x_out, ref)
x_out, fea_grad_std = 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)
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)
ref_stds.append(rstd)
self.attentions = [a1, a2, a3, a4]
self.noise_stds = torch.stack(noise_stds).mean().detach().cpu()
self.ref_stds = torch.stack(ref_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 % 200 == 0:
output_path = os.path.join(experiments_path, "attention_maps", "a%i")
prefix = "attention_map_%i_%%i.png" % (step,)
[save_attention_to_image_rgb(output_path % (i,), self.attentions[i], self.transformation_counts, prefix, step) for i in range(len(self.attentions))]
def get_debug_values(self, step, net_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,
"reference_branch_std_dev": self.ref_stds,
"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
class Spsr4(nn.Module):
def __init__(self, in_nc, out_nc, nf, xforms=8, upscale=4, init_temperature=10):
super(Spsr4, self).__init__()
n_upscale = int(math.log(upscale, 2))
# switch options
transformation_filters = nf
self.transformation_counts = xforms
multiplx_fn = functools.partial(EmbeddingMultiplexer, transformation_filters)
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)
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)
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.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)
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)
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
def forward(self, x, embedding):
noise_stds = []
x_grad = self.get_g_nopadding(x)
x = self.model_fea_conv(x)
x1 = x
x1, a1 = self.sw1(x1, True, identity=x, att_in=(x1, embedding))
x2 = x1
x2, nstd = self.noise_ref_join(x2, torch.randn_like(x2))
x2, a2 = self.sw2(x2, True, 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, True, 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, True, 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 % 200 == 0:
output_path = os.path.join(experiments_path, "attention_maps", "a%i")
prefix = "attention_map_%i_%%i.png" % (step,)
[save_attention_to_image_rgb(output_path % (i,), self.attentions[i], self.transformation_counts, prefix, step) for i in range(len(self.attentions))]
def get_debug_values(self, step, net_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
class Spsr5(nn.Module):
def __init__(self, in_nc, out_nc, nf, xforms=8, upscale=4, 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)
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)
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.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
def forward(self, x, embedding):
noise_stds = []
x_grad = self.get_g_nopadding(x)
x = self.model_fea_conv(x)
x1 = x
x1, a1 = self.sw1(x1, True, identity=x, att_in=(x1, embedding))
x2 = x1
x2, nstd = self.noise_ref_join(x2, torch.randn_like(x2))
x2, a2 = self.sw2(x2, True, 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, True, 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, True, 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 % 200 == 0:
output_path = os.path.join(experiments_path, "attention_maps", "a%i")
prefix = "attention_map_%i_%%i.png" % (step,)
[save_attention_to_image_rgb(output_path % (i,), self.attentions[i], self.transformation_counts, prefix, step) for i in range(len(self.attentions))]
def get_debug_values(self, step, net_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