549 lines
25 KiB
Python
549 lines
25 KiB
Python
import math
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from models.archs import SPSR_util as B
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from .RRDBNet_arch import RRDB
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from models.archs.arch_util import ConvGnLelu, UpconvBlock
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from models.archs.SwitchedResidualGenerator_arch import MultiConvBlock, ConvBasisMultiplexer, ConfigurableSwitchComputer
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from switched_conv_util import save_attention_to_image_rgb
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from switched_conv import compute_attention_specificity
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import functools
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import os
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class ImageGradient(nn.Module):
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def __init__(self):
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super(ImageGradient, self).__init__()
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kernel_v = [[0, -1, 0],
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[0, 0, 0],
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[0, 1, 0]]
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kernel_h = [[0, 0, 0],
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[-1, 0, 1],
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[0, 0, 0]]
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kernel_h = torch.FloatTensor(kernel_h).unsqueeze(0).unsqueeze(0)
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kernel_v = torch.FloatTensor(kernel_v).unsqueeze(0).unsqueeze(0)
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self.weight_h = nn.Parameter(data = kernel_h, requires_grad = False).cuda()
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self.weight_v = nn.Parameter(data = kernel_v, requires_grad = False).cuda()
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def forward(self, x):
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x0 = x[:, 0]
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x1 = x[:, 1]
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x2 = x[:, 2]
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x0_v = F.conv2d(x0.unsqueeze(1), self.weight_v, padding=2)
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x0_h = F.conv2d(x0.unsqueeze(1), self.weight_h, padding=2)
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x1_v = F.conv2d(x1.unsqueeze(1), self.weight_v, padding=2)
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x1_h = F.conv2d(x1.unsqueeze(1), self.weight_h, padding=2)
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x2_v = F.conv2d(x2.unsqueeze(1), self.weight_v, padding=2)
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x2_h = F.conv2d(x2.unsqueeze(1), self.weight_h, padding=2)
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x0 = torch.sqrt(torch.pow(x0_v, 2) + torch.pow(x0_h, 2) + 1e-6)
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x1 = torch.sqrt(torch.pow(x1_v, 2) + torch.pow(x1_h, 2) + 1e-6)
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x2 = torch.sqrt(torch.pow(x2_v, 2) + torch.pow(x2_h, 2) + 1e-6)
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x = torch.cat([x0, x1, x2], dim=1)
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return x
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class ImageGradientNoPadding(nn.Module):
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def __init__(self):
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super(ImageGradientNoPadding, self).__init__()
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kernel_v = [[0, -1, 0],
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[0, 0, 0],
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[0, 1, 0]]
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kernel_h = [[0, 0, 0],
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[-1, 0, 1],
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[0, 0, 0]]
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kernel_h = torch.FloatTensor(kernel_h).unsqueeze(0).unsqueeze(0)
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kernel_v = torch.FloatTensor(kernel_v).unsqueeze(0).unsqueeze(0)
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self.weight_h = nn.Parameter(data = kernel_h, requires_grad = False)
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self.weight_v = nn.Parameter(data = kernel_v, requires_grad = False)
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def forward(self, x):
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x_list = []
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for i in range(x.shape[1]):
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x_i = x[:, i]
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x_i_v = F.conv2d(x_i.unsqueeze(1), self.weight_v, padding=1)
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x_i_h = F.conv2d(x_i.unsqueeze(1), self.weight_h, padding=1)
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x_i = torch.sqrt(torch.pow(x_i_v, 2) + torch.pow(x_i_h, 2) + 1e-6)
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x_list.append(x_i)
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x = torch.cat(x_list, dim = 1)
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return x
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####################
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# Generator
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####################
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class SPSRNet(nn.Module):
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def __init__(self, in_nc, out_nc, nf, nb, gc=32, upscale=4, norm_type=None, \
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act_type='leakyrelu', mode='CNA', upsample_mode='upconv'):
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super(SPSRNet, self).__init__()
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n_upscale = int(math.log(upscale, 2))
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if upscale == 3:
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n_upscale = 1
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fea_conv = B.conv_block(in_nc, nf, kernel_size=3, norm_type=None, act_type=None)
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rb_blocks = [RRDB(nf, gc=32) for _ in range(nb)]
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LR_conv = B.conv_block(nf, nf, kernel_size=3, norm_type=norm_type, act_type=None, mode=mode)
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if upsample_mode == 'upconv':
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upsample_block = B.upconv_block
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elif upsample_mode == 'pixelshuffle':
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upsample_block = B.pixelshuffle_block
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else:
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raise NotImplementedError('upsample mode [{:s}] is not found'.format(upsample_mode))
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if upscale == 3:
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upsampler = upsample_block(nf, nf, 3, act_type=act_type)
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else:
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upsampler = [upsample_block(nf, nf, act_type=act_type) for _ in range(n_upscale)]
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self.HR_conv0_new = B.conv_block(nf, nf, kernel_size=3, norm_type=None, act_type=act_type)
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self.HR_conv1_new = B.conv_block(nf, nf, kernel_size=3, norm_type=None, act_type=None)
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self.model = B.sequential(fea_conv, B.ShortcutBlock(B.sequential(*rb_blocks, LR_conv)),\
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*upsampler, self.HR_conv0_new)
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self.get_g_nopadding = ImageGradientNoPadding()
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self.b_fea_conv = B.conv_block(in_nc, nf, kernel_size=3, norm_type=None, act_type=None)
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self.b_concat_1 = B.conv_block(2*nf, nf, kernel_size=3, norm_type=None, act_type = None)
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self.b_block_1 = RRDB(nf*2, gc=32)
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self.b_concat_2 = B.conv_block(2*nf, nf, kernel_size=3, norm_type=None, act_type = None)
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self.b_block_2 = RRDB(nf*2, gc=32)
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self.b_concat_3 = B.conv_block(2*nf, nf, kernel_size=3, norm_type=None, act_type = None)
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self.b_block_3 = RRDB(nf*2, gc=32)
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self.b_concat_4 = B.conv_block(2*nf, nf, kernel_size=3, norm_type=None, act_type = None)
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self.b_block_4 = RRDB(nf*2, gc=32)
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self.b_LR_conv = B.conv_block(nf, nf, kernel_size=3, norm_type=norm_type, act_type=None, mode=mode)
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if upsample_mode == 'upconv':
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upsample_block = B.upconv_block
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elif upsample_mode == 'pixelshuffle':
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upsample_block = B.pixelshuffle_block
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else:
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raise NotImplementedError('upsample mode [{:s}] is not found'.format(upsample_mode))
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if upscale == 3:
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b_upsampler = upsample_block(nf, nf, 3, act_type=act_type)
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else:
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b_upsampler = [upsample_block(nf, nf, act_type=act_type) for _ in range(n_upscale)]
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b_HR_conv0 = B.conv_block(nf, nf, kernel_size=3, norm_type=None, act_type=act_type)
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b_HR_conv1 = B.conv_block(nf, nf, kernel_size=3, norm_type=None, act_type=None)
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self.b_module = B.sequential(*b_upsampler, b_HR_conv0, b_HR_conv1)
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self.conv_w = B.conv_block(nf, out_nc, kernel_size=1, norm_type=None, act_type=None)
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# Note: "_branch_pretrain" is a special tag used to denote parameters that get pretrained before the rest.
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self._branch_pretrain_concat = B.conv_block(nf*2, nf, kernel_size=3, norm_type=None, act_type=None)
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self._branch_pretrain_block = RRDB(nf*2, gc=32)
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self._branch_pretrain_HR_conv0 = B.conv_block(nf, nf, kernel_size=3, norm_type=None, act_type=act_type)
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self._branch_pretrain_HR_conv1 = B.conv_block(nf, out_nc, kernel_size=3, norm_type=None, act_type=None)
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def forward(self, x):
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x_grad = self.get_g_nopadding(x)
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x = self.model[0](x)
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x, block_list = self.model[1](x)
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x_ori = x
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for i in range(5):
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x = block_list[i](x)
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x_fea1 = x
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for i in range(5):
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x = block_list[i+5](x)
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x_fea2 = x
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for i in range(5):
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x = block_list[i+10](x)
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x_fea3 = x
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for i in range(5):
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x = block_list[i+15](x)
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x_fea4 = x
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x = block_list[20:](x)
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#short cut
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x = x_ori+x
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x= self.model[2:](x)
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x = self.HR_conv1_new(x)
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x_b_fea = self.b_fea_conv(x_grad)
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x_cat_1 = torch.cat([x_b_fea, x_fea1], dim=1)
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x_cat_1 = self.b_block_1(x_cat_1)
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x_cat_1 = self.b_concat_1(x_cat_1)
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x_cat_2 = torch.cat([x_cat_1, x_fea2], dim=1)
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x_cat_2 = self.b_block_2(x_cat_2)
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x_cat_2 = self.b_concat_2(x_cat_2)
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x_cat_3 = torch.cat([x_cat_2, x_fea3], dim=1)
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x_cat_3 = self.b_block_3(x_cat_3)
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x_cat_3 = self.b_concat_3(x_cat_3)
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x_cat_4 = torch.cat([x_cat_3, x_fea4], dim=1)
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x_cat_4 = self.b_block_4(x_cat_4)
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x_cat_4 = self.b_concat_4(x_cat_4)
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x_cat_4 = self.b_LR_conv(x_cat_4)
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#short cut
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x_cat_4 = x_cat_4+x_b_fea
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x_branch = self.b_module(x_cat_4)
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x_out_branch = self.conv_w(x_branch)
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########
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x_branch_d = x_branch
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x__branch_pretrain_cat = torch.cat([x_branch_d, x], dim=1)
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x__branch_pretrain_cat = self._branch_pretrain_block(x__branch_pretrain_cat)
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x_out = self._branch_pretrain_concat(x__branch_pretrain_cat)
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x_out = self._branch_pretrain_HR_conv0(x_out)
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x_out = self._branch_pretrain_HR_conv1(x_out)
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#########
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return x_out_branch, x_out, x_grad
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class SPSRNetSimplified(nn.Module):
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def __init__(self, in_nc, out_nc, nf, nb, upscale=4):
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super(SPSRNetSimplified, self).__init__()
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n_upscale = int(math.log(upscale, 2))
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# Feature branch
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self.model_fea_conv = ConvGnLelu(in_nc, nf, kernel_size=3, norm=False, activation=False)
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self.model_shortcut_blk = nn.Sequential(*[RRDB(nf, gc=32) for _ in range(nb)])
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self.feature_lr_conv = ConvGnLelu(nf, nf, kernel_size=3, norm=False, activation=False)
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self.model_upsampler = nn.Sequential(*[UpconvBlock(nf, nf, block=ConvGnLelu, norm=False, activation=False, bias=False) for _ in range(n_upscale)])
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self.feature_hr_conv1 = ConvGnLelu(nf, nf, kernel_size=3, norm=False, activation=True, bias=False)
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self.feature_hr_conv2 = ConvGnLelu(nf, nf, kernel_size=3, norm=False, activation=False, bias=False)
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# Grad branch
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self.get_g_nopadding = ImageGradientNoPadding()
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self.b_fea_conv = ConvGnLelu(in_nc, nf, kernel_size=3, norm=False, activation=False, bias=False)
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self.b_concat_decimate_1 = ConvGnLelu(2 * nf, nf, kernel_size=1, norm=False, activation=False, bias=False)
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self.b_proc_block_1 = RRDB(nf, gc=32)
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self.b_concat_decimate_2 = ConvGnLelu(2 * nf, nf, kernel_size=1, norm=False, activation=False, bias=False)
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self.b_proc_block_2 = RRDB(nf, gc=32)
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self.b_concat_decimate_3 = ConvGnLelu(2 * nf, nf, kernel_size=1, norm=False, activation=False, bias=False)
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self.b_proc_block_3 = RRDB(nf, gc=32)
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self.b_concat_decimate_4 = ConvGnLelu(2 * nf, nf, kernel_size=1, norm=False, activation=False, bias=False)
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self.b_proc_block_4 = RRDB(nf, gc=32)
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# Upsampling
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self.grad_lr_conv = ConvGnLelu(nf, nf, kernel_size=3, norm=False, activation=True, bias=False)
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b_upsampler = nn.Sequential(*[UpconvBlock(nf, nf, block=ConvGnLelu, norm=False, activation=False, bias=False) for _ in range(n_upscale)])
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grad_hr_conv1 = ConvGnLelu(nf, nf, kernel_size=3, norm=False, activation=True, bias=False)
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grad_hr_conv2 = ConvGnLelu(nf, nf, kernel_size=3, norm=False, activation=False, bias=False)
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self.branch_upsample = B.sequential(*b_upsampler, grad_hr_conv1, grad_hr_conv2)
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# Conv used to output grad branch shortcut.
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self.grad_branch_output_conv = ConvGnLelu(nf, out_nc, kernel_size=1, norm=False, activation=False, bias=False)
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# Conjoin branch.
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# Note: "_branch_pretrain" is a special tag used to denote parameters that get pretrained before the rest.
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self._branch_pretrain_concat = ConvGnLelu(nf * 2, nf, kernel_size=1, norm=False, activation=False, bias=False)
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self._branch_pretrain_block = RRDB(nf * 2, gc=32)
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self._branch_pretrain_HR_conv0 = ConvGnLelu(nf, nf, kernel_size=3, norm=False, activation=True, bias=False)
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self._branch_pretrain_HR_conv1 = ConvGnLelu(nf, out_nc, kernel_size=3, norm=False, activation=False, bias=False)
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def forward(self, x):
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x_grad = self.get_g_nopadding(x)
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x = self.model_fea_conv(x)
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x_ori = x
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for i in range(5):
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x = self.model_shortcut_blk[i](x)
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x_fea1 = x
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for i in range(5):
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x = self.model_shortcut_blk[i + 5](x)
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x_fea2 = x
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for i in range(5):
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x = self.model_shortcut_blk[i + 10](x)
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x_fea3 = x
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for i in range(5):
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x = self.model_shortcut_blk[i + 15](x)
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x_fea4 = x
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x = self.model_shortcut_blk[20:](x)
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x = self.feature_lr_conv(x)
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# short cut
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x = x_ori + x
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x = self.model_upsampler(x)
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x = self.feature_hr_conv1(x)
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x = self.feature_hr_conv2(x)
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x_b_fea = self.b_fea_conv(x_grad)
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x_cat_1 = torch.cat([x_b_fea, x_fea1], dim=1)
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x_cat_1 = self.b_concat_decimate_1(x_cat_1)
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x_cat_1 = self.b_proc_block_1(x_cat_1)
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x_cat_2 = torch.cat([x_cat_1, x_fea2], dim=1)
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x_cat_2 = self.b_concat_decimate_2(x_cat_2)
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x_cat_2 = self.b_proc_block_2(x_cat_2)
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x_cat_3 = torch.cat([x_cat_2, x_fea3], dim=1)
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x_cat_3 = self.b_concat_decimate_3(x_cat_3)
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x_cat_3 = self.b_proc_block_3(x_cat_3)
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x_cat_4 = torch.cat([x_cat_3, x_fea4], dim=1)
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x_cat_4 = self.b_concat_decimate_4(x_cat_4)
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x_cat_4 = self.b_proc_block_4(x_cat_4)
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x_cat_4 = self.grad_lr_conv(x_cat_4)
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# short cut
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x_cat_4 = x_cat_4 + x_b_fea
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x_branch = self.branch_upsample(x_cat_4)
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x_out_branch = self.grad_branch_output_conv(x_branch)
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########
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x_branch_d = x_branch
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x__branch_pretrain_cat = torch.cat([x_branch_d, x], dim=1)
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x__branch_pretrain_cat = self._branch_pretrain_block(x__branch_pretrain_cat)
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x_out = self._branch_pretrain_concat(x__branch_pretrain_cat)
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x_out = self._branch_pretrain_HR_conv0(x_out)
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x_out = self._branch_pretrain_HR_conv1(x_out)
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#########
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return x_out_branch, x_out, x_grad
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class SPSRNetSimplifiedNoSkip(nn.Module):
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def __init__(self, in_nc, out_nc, nf, nb, upscale=4):
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super(SPSRNetSimplifiedNoSkip, self).__init__()
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n_upscale = int(math.log(upscale, 2))
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# Feature branch
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self.model_fea_conv = ConvGnLelu(in_nc, nf, kernel_size=3, norm=False, activation=False)
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self.model_shortcut_blk = nn.Sequential(*[RRDB(nf, gc=32) for _ in range(nb)])
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self.feature_lr_conv = ConvGnLelu(nf, nf, kernel_size=3, norm=False, activation=False)
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self.model_upsampler = nn.Sequential(*[UpconvBlock(nf, nf, block=ConvGnLelu, norm=False, activation=False, bias=False) for _ in range(n_upscale)])
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self.feature_hr_conv1 = ConvGnLelu(nf, nf, kernel_size=3, norm=False, activation=True, bias=False)
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self.feature_hr_conv2 = ConvGnLelu(nf, nf, kernel_size=3, norm=False, activation=False, bias=False)
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# Grad branch
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self.get_g_nopadding = ImageGradientNoPadding()
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self.b_fea_conv = ConvGnLelu(in_nc, nf, kernel_size=3, norm=False, activation=False, bias=False)
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self.b_concat_decimate_1 = ConvGnLelu(2 * nf, nf, kernel_size=1, norm=False, activation=False, bias=False)
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self.b_proc_block_1 = RRDB(nf, gc=32)
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self.b_concat_decimate_2 = ConvGnLelu(2 * nf, nf, kernel_size=1, norm=False, activation=False, bias=False)
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self.b_proc_block_2 = RRDB(nf, gc=32)
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self.b_concat_decimate_3 = ConvGnLelu(2 * nf, nf, kernel_size=1, norm=False, activation=False, bias=False)
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self.b_proc_block_3 = RRDB(nf, gc=32)
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self.b_concat_decimate_4 = ConvGnLelu(2 * nf, nf, kernel_size=1, norm=False, activation=False, bias=False)
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self.b_proc_block_4 = RRDB(nf, gc=32)
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# Upsampling
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self.grad_lr_conv = ConvGnLelu(nf, nf, kernel_size=3, norm=False, activation=True, bias=False)
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b_upsampler = nn.Sequential(*[UpconvBlock(nf, nf, block=ConvGnLelu, norm=False, activation=False, bias=False) for _ in range(n_upscale)])
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grad_hr_conv1 = ConvGnLelu(nf, nf, kernel_size=3, norm=False, activation=True, bias=False)
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grad_hr_conv2 = ConvGnLelu(nf, nf, kernel_size=3, norm=False, activation=False, bias=False)
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self.branch_upsample = B.sequential(*b_upsampler, grad_hr_conv1, grad_hr_conv2)
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# Conv used to output grad branch shortcut.
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|
self.grad_branch_output_conv = ConvGnLelu(nf, out_nc, kernel_size=1, norm=False, activation=False, bias=False)
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|
|
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# Conjoin branch.
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|
# Note: "_branch_pretrain" is a special tag used to denote parameters that get pretrained before the rest.
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|
self._branch_pretrain_concat = ConvGnLelu(nf * 2, nf, kernel_size=1, norm=False, activation=False, bias=False)
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self._branch_pretrain_block = RRDB(nf * 2, gc=32)
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self._branch_pretrain_HR_conv0 = ConvGnLelu(nf, nf, kernel_size=3, norm=False, activation=True, bias=False)
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self._branch_pretrain_HR_conv1 = ConvGnLelu(nf, out_nc, kernel_size=3, norm=False, activation=False, bias=False)
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|
|
|
def forward(self, x):
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x_grad = self.get_g_nopadding(x)
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x = self.model_fea_conv(x)
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|
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x_ori = x
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|
for i in range(5):
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x = self.model_shortcut_blk[i](x)
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for i in range(5):
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x = self.model_shortcut_blk[i + 5](x)
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|
for i in range(5):
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x = self.model_shortcut_blk[i + 10](x)
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|
for i in range(5):
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x = self.model_shortcut_blk[i + 15](x)
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|
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x = self.model_shortcut_blk[20:](x)
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x = self.feature_lr_conv(x)
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|
|
|
# short cut
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|
x = x_ori + x
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|
x = self.model_upsampler(x)
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|
x = self.feature_hr_conv1(x)
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|
x = self.feature_hr_conv2(x)
|
|
|
|
x_b_fea = self.b_fea_conv(x_grad)
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|
x_cat_1 = self.b_proc_block_1(x_b_fea)
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|
x_cat_2 = self.b_proc_block_2(x_cat_1)
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|
x_cat_3 = self.b_proc_block_3(x_cat_2)
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|
x_cat_4 = self.b_proc_block_4(x_cat_3)
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|
x_cat_4 = x_cat_4 + x_b_fea
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|
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)
|
|
|
|
########
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|
x_branch_d = x_branch
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|
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):
|
|
def __init__(self, in_nc, out_nc, nf, upscale=4):
|
|
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
|
|
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.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,
|
|
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)
|
|
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,
|
|
transform_count=self.transformation_counts, init_temp=10,
|
|
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)
|
|
x__branch_pretrain_cat = self._branch_pretrain_concat(x__branch_pretrain_cat)
|
|
x__branch_pretrain_cat, a4 = self._branch_pretrain_sw(x__branch_pretrain_cat, True)
|
|
x_out = self._branch_pretrain_HR_conv0(x__branch_pretrain_cat)
|
|
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]
|
|
|
|
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
|