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DL-Art-School/codes/models/archs/SPSR_arch.py

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Integrate SPSR into SRGAN_model SPSR_model really isn't that different from SRGAN_model. Rather than continuing to re-implement everything I've done in SRGAN_model, port the new stuff from SPSR over. This really demonstrates the need to refactor SRGAN_model a bit to make it cleaner. It is quite the beast these days..
2020-08-02 18:55:08 +00:00
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
Several new spsr nets
2020-08-05 16:01:24 +00:00
from models.archs.arch_util import ConvGnLelu, UpconvBlock
from models.archs.SwitchedResidualGenerator_arch import MultiConvBlock, ConvBasisMultiplexer, ConfigurableSwitchComputer
from switched_conv_util import save_attention_to_image_rgb
Finish up spsr_switched
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from switched_conv import compute_attention_specificity
Several new spsr nets
2020-08-05 16:01:24 +00:00
import functools
spsr_switched missing import
2020-08-08 03:05:29 +00:00
import os
Integrate SPSR into SRGAN_model SPSR_model really isn't that different from SRGAN_model. Rather than continuing to re-implement everything I've done in SRGAN_model, port the new stuff from SPSR over. This really demonstrates the need to refactor SRGAN_model a bit to make it cleaner. It is quite the beast these days..
2020-08-02 18:55:08 +00:00
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'):
super(SPSRNet, self).__init__()
n_upscale = int(math.log(upscale, 2))
if upscale == 3:
n_upscale = 1
fea_conv = B.conv_block(in_nc, 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':
Add simplified SPSR architecture Basically just cleaning up the code, removing some bad conventions, and reducing complexity somewhat so that I can play around with this arch a bit more easily.
2020-08-03 16:25:37 +00:00
upsample_block = B.upconv_block
Integrate SPSR into SRGAN_model SPSR_model really isn't that different from SRGAN_model. Rather than continuing to re-implement everything I've done in SRGAN_model, port the new stuff from SPSR over. This really demonstrates the need to refactor SRGAN_model a bit to make it cleaner. It is quite the beast these days..
2020-08-02 18:55:08 +00:00
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':
Add simplified SPSR architecture Basically just cleaning up the code, removing some bad conventions, and reducing complexity somewhat so that I can play around with this arch a bit more easily.
2020-08-03 16:25:37 +00:00
upsample_block = B.upconv_block
Integrate SPSR into SRGAN_model SPSR_model really isn't that different from SRGAN_model. Rather than continuing to re-implement everything I've done in SRGAN_model, port the new stuff from SPSR over. This really demonstrates the need to refactor SRGAN_model a bit to make it cleaner. It is quite the beast these days..
2020-08-02 18:55:08 +00:00
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):
x_grad = self.get_g_nopadding(x)
x = self.model[0](x)
x, block_list = self.model[1](x)
x_ori = x
for i in range(5):
x = block_list[i](x)
x_fea1 = x
for i in range(5):
x = block_list[i+5](x)
x_fea2 = x
for i in range(5):
x = block_list[i+10](x)
x_fea3 = x
for i in range(5):
x = block_list[i+15](x)
x_fea4 = x
x = block_list[20:](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
Add simplified SPSR architecture Basically just cleaning up the code, removing some bad conventions, and reducing complexity somewhat so that I can play around with this arch a bit more easily.
2020-08-03 16:25:37 +00:00
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)
Several new spsr nets
2020-08-05 16:01:24 +00:00
Add simplified SPSR architecture Basically just cleaning up the code, removing some bad conventions, and reducing complexity somewhat so that I can play around with this arch a bit more easily.
2020-08-03 16:25:37 +00:00
# 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
Several new spsr nets
2020-08-05 16:01:24 +00:00
class SPSRNetSimplifiedNoSkip(nn.Module):
def __init__(self, in_nc, out_nc, nf, nb, upscale=4):
super(SPSRNetSimplifiedNoSkip, 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)
for i in range(5):
x = self.model_shortcut_blk[i + 5](x)
for i in range(5):
x = self.model_shortcut_blk[i + 10](x)
for i in range(5):
x = self.model_shortcut_blk[i + 15](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 = self.b_proc_block_1(x_b_fea)
x_cat_2 = self.b_proc_block_2(x_cat_1)
x_cat_3 = self.b_proc_block_3(x_cat_2)
x_cat_4 = self.b_proc_block_4(x_cat_3)
x_cat_4 = x_cat_4 + x_b_fea
x_cat_4 = self.grad_lr_conv(x_cat_4)
# short cut
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 SwitchedSpsr(nn.Module):
Finish up spsr_switched
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def __init__(self, in_nc, out_nc, nf, upscale=4):
Several new spsr nets
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super(SwitchedSpsr, self).__init__()
n_upscale = int(math.log(upscale, 2))
# switch options
transformation_filters = nf
switch_filters = nf
switch_reductions = 3
switch_processing_layers = 2
More attention fixes for switched_spsr
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self.transformation_counts = 8
Several new spsr nets
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multiplx_fn = functools.partial(ConvBasisMultiplexer, transformation_filters, switch_filters, switch_reductions,
More attention fixes for switched_spsr
2020-08-08 03:11:50 +00:00
switch_processing_layers, self.transformation_counts)
Several new spsr nets
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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),
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transformation_filters, kernel_size=3, depth=3,
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weight_init_factor=.1)
# Feature branch
self.model_fea_conv = ConvGnLelu(in_nc, nf, kernel_size=3, norm=False, activation=False)
self.sw1 = ConfigurableSwitchComputer(transformation_filters, multiplx_fn,
pre_transform_block=pretransform_fn, transform_block=transform_fn,
attention_norm=True,
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transform_count=self.transformation_counts, init_temp=10,
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add_scalable_noise_to_transforms=True)
self.sw2 = ConfigurableSwitchComputer(transformation_filters, multiplx_fn,
pre_transform_block=pretransform_fn, transform_block=transform_fn,
attention_norm=True,
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transform_count=self.transformation_counts, init_temp=10,
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add_scalable_noise_to_transforms=True)
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.sw_grad = ConfigurableSwitchComputer(transformation_filters, multiplx_fn,
pre_transform_block=pretransform_fn, transform_block=transform_fn,
attention_norm=True,
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transform_count=self.transformation_counts, init_temp=10,
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add_scalable_noise_to_transforms=True)
# 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_sw = ConfigurableSwitchComputer(transformation_filters, multiplx_fn,
pre_transform_block=pretransform_fn, transform_block=transform_fn,
attention_norm=True,
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transform_count=self.transformation_counts, init_temp=10,
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add_scalable_noise_to_transforms=True)
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)
self.switches = [self.sw1, self.sw2, self.sw_grad, self._branch_pretrain_sw]
self.attentions = None
self.init_temperature = 10
self.final_temperature_step = 10000
def forward(self, x):
x_grad = self.get_g_nopadding(x)
x = self.model_fea_conv(x)
x1, a1 = self.sw1(x, True)
x2, a2 = self.sw2(x1, True)
x_fea = self.feature_lr_conv(x2)
x_fea = self.model_upsampler(x_fea)
x_fea = self.feature_hr_conv1(x_fea)
x_fea = self.feature_hr_conv2(x_fea)
x_b_fea = self.b_fea_conv(x_grad)
x_grad, a3 = self.sw_grad(x_b_fea, att_in=x1, output_attention_weights=True)
x_grad = self.grad_lr_conv(x_grad)
x_grad = self.branch_upsample(x_grad)
x_out_branch = self.grad_branch_output_conv(x_grad)
x__branch_pretrain_cat = torch.cat([x_grad, x_fea], dim=1)
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x__branch_pretrain_cat = self._branch_pretrain_concat(x__branch_pretrain_cat)
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x__branch_pretrain_cat, a4 = self._branch_pretrain_sw(x__branch_pretrain_cat, True)
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x_out = self._branch_pretrain_HR_conv0(x__branch_pretrain_cat)
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x_out = self._branch_pretrain_HR_conv1(x_out)
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self.attentions = [a1, a2, a3, a4]
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return x_out_branch, x_out, x_grad
def set_temperature(self, temp):
[sw.set_temperature(temp) for sw in self.switches]
LR switched SPSR arch This variant doesn't do conv processing at HR, which should save a ton of memory in inference. Lets see how it works.
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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 % 10 == 0:
output_path = os.path.join(experiments_path, "attention_maps", "a%i")
prefix = "attention_map_%i_%%i.png" % (step,)
[save_attention_to_image_rgb(output_path % (i,), self.attentions[i], self.transformation_counts, prefix, step) for i in range(len(self.attentions))]
def get_debug_values(self, step):
temp = self.switches[0].switch.temperature
mean_hists = [compute_attention_specificity(att, 2) for att in self.attentions]
means = [i[0] for i in mean_hists]
hists = [i[1].clone().detach().cpu().flatten() for i in mean_hists]
val = {"switch_temperature": temp}
for i in range(len(means)):
val["switch_%i_specificity" % (i,)] = means[i]
val["switch_%i_histogram" % (i,)] = hists[i]
return val
class SwitchedSpsrLr(nn.Module):
def __init__(self, in_nc, out_nc, nf, upscale=4):
super(SwitchedSpsrLr, self).__init__()
n_upscale = int(math.log(upscale, 2))
# switch options
transformation_filters = nf
switch_filters = nf
switch_reductions = 3
switch_processing_layers = 2
self.transformation_counts = 8
multiplx_fn = functools.partial(ConvBasisMultiplexer, transformation_filters, switch_filters, switch_reductions,
switch_processing_layers, self.transformation_counts)
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.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=10,
add_scalable_noise_to_transforms=True)
self.sw2 = ConfigurableSwitchComputer(transformation_filters, multiplx_fn,
pre_transform_block=pretransform_fn, transform_block=transform_fn,
attention_norm=True,
transform_count=self.transformation_counts, init_temp=10,
add_scalable_noise_to_transforms=True)
self.feature_lr_conv = ConvGnLelu(nf, nf, kernel_size=3, norm=False, activation=False)
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.sw_grad = ConfigurableSwitchComputer(transformation_filters, multiplx_fn,
pre_transform_block=pretransform_fn, transform_block=transform_fn,
attention_norm=True,
transform_count=self.transformation_counts, init_temp=10,
add_scalable_noise_to_transforms=True)
# Upsampling
self.grad_lr_conv = ConvGnLelu(nf, nf, kernel_size=3, norm=False, activation=True, bias=False)
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(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.
transform_fn_cat = functools.partial(MultiConvBlock, transformation_filters * 2, int(transformation_filters * 1.5),
transformation_filters, kernel_size=3, depth=4,
weight_init_factor=.1)
pretransform_fn_cat = functools.partial(ConvGnLelu, transformation_filters * 2, transformation_filters * 2, norm=False, bias=False, weight_init_factor=.1)
self._branch_pretrain_sw = ConfigurableSwitchComputer(transformation_filters, multiplx_fn,
pre_transform_block=pretransform_fn_cat, transform_block=transform_fn_cat,
attention_norm=True,
transform_count=self.transformation_counts, init_temp=10,
add_scalable_noise_to_transforms=True)
self.upsample = nn.Sequential(*[UpconvBlock(nf, nf, block=ConvGnLelu, norm=False, activation=False, bias=False) for _ in range(n_upscale)])
self.upsample_grad = nn.Sequential(*[UpconvBlock(nf, nf, block=ConvGnLelu, norm=False, activation=False, bias=False) for _ in range(n_upscale)])
self._branch_pretrain_lr_conv = ConvGnLelu(nf, nf, kernel_size=3, norm=False, activation=False)
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)
self.switches = [self.sw1, self.sw2, self.sw_grad, self._branch_pretrain_sw]
self.attentions = None
self.init_temperature = 10
self.final_temperature_step = 10000
def forward(self, x):
x_grad = self.get_g_nopadding(x)
x = self.model_fea_conv(x)
x1, a1 = self.sw1(x, True)
x2, a2 = self.sw2(x1, True)
x_fea = self.feature_lr_conv(x2)
x_fea = self.feature_hr_conv1(x_fea)
x_fea = self.feature_hr_conv2(x_fea)
x_b_fea = self.b_fea_conv(x_grad)
x_grad, a3 = self.sw_grad(x_b_fea, att_in=x1, output_attention_weights=True)
x_grad = self.grad_lr_conv(x_grad)
x_grad = self.branch_upsample(x_grad)
x_out_branch = self.upsample_grad(x_grad)
x_out_branch = self.grad_branch_output_conv(x_out_branch)
x__branch_pretrain_cat = torch.cat([x_grad, x_fea], dim=1)
x__branch_pretrain_cat, a4 = self._branch_pretrain_sw(x__branch_pretrain_cat, att_in=x_fea, identity=x_fea, output_attention_weights=True)
x_out = self._branch_pretrain_lr_conv(x__branch_pretrain_cat)
x_out = self.upsample(x_out)
x_out = self._branch_pretrain_HR_conv0(x_out)
x_out = self._branch_pretrain_HR_conv1(x_out)
self.attentions = [a1, a2, a3, a4]
return x_out_branch, x_out, x_grad
def set_temperature(self, temp):
[sw.set_temperature(temp) for sw in self.switches]
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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)
More attention fixes for switched_spsr
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if step % 10 == 0:
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output_path = os.path.join(experiments_path, "attention_maps", "a%i")
prefix = "attention_map_%i_%%i.png" % (step,)
[save_attention_to_image_rgb(output_path % (i,), self.attentions[i], self.transformation_counts, prefix, step) for i in range(len(self.attentions))]
def get_debug_values(self, step):
temp = self.switches[0].switch.temperature
mean_hists = [compute_attention_specificity(att, 2) for att in self.attentions]
means = [i[0] for i in mean_hists]
hists = [i[1].clone().detach().cpu().flatten() for i in mean_hists]
val = {"switch_temperature": temp}
for i in range(len(means)):
val["switch_%i_specificity" % (i,)] = means[i]
val["switch_%i_histogram" % (i,)] = hists[i]
return val
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