import functools
import os
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision
from utils.util import checkpoint
from models.archs import SPSR_util as B
from models.archs.SwitchedResidualGenerator_arch import ConfigurableSwitchComputer, ReferenceImageBranch, \
QueryKeyMultiplexer, QueryKeyPyramidMultiplexer
from models.archs.arch_util import ConvGnLelu, UpconvBlock, MultiConvBlock, ReferenceJoinBlock
from switched_conv.switched_conv import compute_attention_specificity
from switched_conv.switched_conv_util import save_attention_to_image_rgb
from .RRDBNet_arch import RRDB
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 SPSRNetSimplified(nn.Module):
def __init__(self, in_nc, out_nc, nf, nb, upscale=4):
super(SPSRNetSimplified, self).__init__()
n_upscale = int(math.log(upscale, 2))
# Feature branch
self.model_fea_conv = ConvGnLelu(in_nc, nf, kernel_size=3, norm=False, activation=False)
self.model_shortcut_blk = nn.Sequential(*[RRDB(nf, gc=32) for _ in range(nb)])
self.feature_lr_conv = ConvGnLelu(nf, nf, kernel_size=3, norm=False, activation=False)
self.model_upsampler = nn.Sequential(*[UpconvBlock(nf, nf, block=ConvGnLelu, norm=False, activation=False, bias=False) for _ in range(n_upscale)])
self.feature_hr_conv1 = ConvGnLelu(nf, nf, kernel_size=3, norm=False, activation=True, bias=False)
self.feature_hr_conv2 = ConvGnLelu(nf, nf, kernel_size=3, norm=False, activation=False, bias=False)
# Grad branch
self.get_g_nopadding = ImageGradientNoPadding()
self.b_fea_conv = ConvGnLelu(in_nc, nf, kernel_size=3, norm=False, activation=False, bias=False)
self.b_concat_decimate_1 = ConvGnLelu(2 * nf, nf, kernel_size=1, norm=False, activation=False, bias=False)
self.b_proc_block_1 = RRDB(nf, gc=32)
self.b_concat_decimate_2 = ConvGnLelu(2 * nf, nf, kernel_size=1, norm=False, activation=False, bias=False)
self.b_proc_block_2 = RRDB(nf, gc=32)
self.b_concat_decimate_3 = ConvGnLelu(2 * nf, nf, kernel_size=1, norm=False, activation=False, bias=False)
self.b_proc_block_3 = RRDB(nf, gc=32)
self.b_concat_decimate_4 = ConvGnLelu(2 * nf, nf, kernel_size=1, norm=False, activation=False, bias=False)
self.b_proc_block_4 = RRDB(nf, gc=32)
# Upsampling
self.grad_lr_conv = ConvGnLelu(nf, nf, kernel_size=3, norm=False, activation=True, bias=False)
b_upsampler = nn.Sequential(*[UpconvBlock(nf, nf, block=ConvGnLelu, norm=False, activation=False, bias=False) for _ in range(n_upscale)])
grad_hr_conv1 = ConvGnLelu(nf, nf, kernel_size=3, norm=False, activation=True, bias=False)
grad_hr_conv2 = ConvGnLelu(nf, nf, kernel_size=3, norm=False, activation=False, bias=False)
self.branch_upsample = B.sequential(*b_upsampler, grad_hr_conv1, grad_hr_conv2)
# Conv used to output grad branch shortcut.
self.grad_branch_output_conv = ConvGnLelu(nf, out_nc, kernel_size=1, norm=False, activation=False, bias=False)
# Conjoin branch.
# Note: "_branch_pretrain" is a special tag used to denote parameters that get pretrained before the rest.
self._branch_pretrain_concat = ConvGnLelu(nf * 2, nf, kernel_size=1, norm=False, activation=False, bias=False)
self._branch_pretrain_block = RRDB(nf * 2, gc=32)
self._branch_pretrain_HR_conv0 = ConvGnLelu(nf, nf, kernel_size=3, norm=False, activation=True, bias=False)
self._branch_pretrain_HR_conv1 = ConvGnLelu(nf, out_nc, kernel_size=3, norm=False, activation=False, bias=False)
def forward(self, x):
x_grad = self.get_g_nopadding(x)
x = self.model_fea_conv(x)
x_ori = x
for i in range(5):
x = self.model_shortcut_blk[i](x)
x_fea1 = x
for i in range(5):
x = self.model_shortcut_blk[i + 5](x)
x_fea2 = x
for i in range(5):
x = self.model_shortcut_blk[i + 10](x)
x_fea3 = x
for i in range(5):
x = self.model_shortcut_blk[i + 15](x)
x_fea4 = x
x = self.model_shortcut_blk[20:](x)
x = self.feature_lr_conv(x)
# short cut
x = x_ori + x
x = self.model_upsampler(x)
x = self.feature_hr_conv1(x)
x = self.feature_hr_conv2(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_concat_decimate_1(x_cat_1)
x_cat_1 = self.b_proc_block_1(x_cat_1)
x_cat_2 = torch.cat([x_cat_1, x_fea2], dim=1)
x_cat_2 = self.b_concat_decimate_2(x_cat_2)
x_cat_2 = self.b_proc_block_2(x_cat_2)
x_cat_3 = torch.cat([x_cat_2, x_fea3], dim=1)
x_cat_3 = self.b_concat_decimate_3(x_cat_3)
x_cat_3 = self.b_proc_block_3(x_cat_3)
x_cat_4 = torch.cat([x_cat_3, x_fea4], dim=1)
x_cat_4 = self.b_concat_decimate_4(x_cat_4)
x_cat_4 = self.b_proc_block_4(x_cat_4)
x_cat_4 = self.grad_lr_conv(x_cat_4)
# short cut
x_cat_4 = x_cat_4 + x_b_fea
x_branch = self.branch_upsample(x_cat_4)
x_out_branch = self.grad_branch_output_conv(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 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)
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
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, 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 % 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, True, identity=x)
x2 = x1
x2, a2 = self.sw2(x2, True, 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, 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)
x_out = x2
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 = 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.
class Spsr7(nn.Module):
def __init__(self, in_nc, out_nc, nf, xforms=8, upscale=4, multiplexer_reductions=3, init_temperature=10):
super(Spsr7, self).__init__()
n_upscale = int(math.log(upscale, 2))
# processing the input embedding
self.reference_embedding = ReferenceImageBranch(nf)
# switch options
self.nf = nf
transformation_filters = nf
self.transformation_counts = xforms
multiplx_fn = functools.partial(QueryKeyMultiplexer, transformation_filters, embedding_channels=512, 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)
# 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.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, ref, ref_center, update_attention_norm=True):
# The attention_maps debugger outputs <x>. Save that here.
self.lr = x.detach().cpu()
x_grad = self.get_g_nopadding(x)
ref_code = 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 = self.sw1(x1, True, identity=x, att_in=(x1, ref_embedding))
x2 = x1
x2, a2 = self.sw2(x2, True, identity=x1, att_in=(x2, ref_embedding))
x_grad = self.grad_conv(x_grad)
x_grad_identity = x_grad
x_grad, grad_fea_std = checkpoint(self.grad_ref_join, x_grad, x1)
x_grad, a3 = self.sw_grad(x_grad, True, identity=x_grad_identity, att_in=(x_grad, ref_embedding))
x_grad = checkpoint(self.grad_lr_conv, x_grad)
x_grad = checkpoint(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, 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, ref_embedding))
x_out = checkpoint(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 AttentionBlock(nn.Module):
def __init__(self, nf, num_transforms, multiplexer_reductions, init_temperature=10, has_ref=True):
super(AttentionBlock, self).__init__()
self.nf = nf
self.transformation_counts = num_transforms
multiplx_fn = functools.partial(QueryKeyMultiplexer, nf, embedding_channels=512, reductions=multiplexer_reductions)
transform_fn = functools.partial(MultiConvBlock, nf, int(nf * 1.5),
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)
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, True, identity=x, att_in=(branch, mplex_ref)) + (ref_std,)
else:
return self.switch(x, True, 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