forked from mrq/DL-Art-School
Unite spsr_arch switched gens
Found a pretty good basis model.
This commit is contained in:
James Betker
parent
bdaa67deb7
commit
2d205f52ac
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@ -4,7 +4,7 @@ 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.arch_util import ConvGnLelu, UpconvBlock, ConjoinBlock2
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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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@ -231,452 +231,11 @@ class SPSRNet(nn.Module):
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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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# 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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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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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 = 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)
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# short cut
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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 SwitchedSpsr(nn.Module):
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def __init__(self, in_nc, out_nc, nf, upscale=4):
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super(SwitchedSpsr, self).__init__()
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n_upscale = int(math.log(upscale, 2))
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# switch options
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transformation_filters = nf
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switch_filters = nf
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switch_reductions = 3
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switch_processing_layers = 2
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self.transformation_counts = 8
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multiplx_fn = functools.partial(ConvBasisMultiplexer, transformation_filters, switch_filters, switch_reductions,
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switch_processing_layers, self.transformation_counts)
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pretransform_fn = functools.partial(ConvGnLelu, transformation_filters, transformation_filters, norm=False, bias=False, weight_init_factor=.1)
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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)
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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.sw1 = ConfigurableSwitchComputer(transformation_filters, multiplx_fn,
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pre_transform_block=pretransform_fn, transform_block=transform_fn,
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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)
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self.sw2 = ConfigurableSwitchComputer(transformation_filters, multiplx_fn,
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pre_transform_block=pretransform_fn, transform_block=transform_fn,
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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)
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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.sw_grad = ConfigurableSwitchComputer(transformation_filters, multiplx_fn,
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pre_transform_block=pretransform_fn, transform_block=transform_fn,
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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)
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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_sw = ConfigurableSwitchComputer(transformation_filters, multiplx_fn,
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pre_transform_block=pretransform_fn, transform_block=transform_fn,
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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)
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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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self.switches = [self.sw1, self.sw2, self.sw_grad, self._branch_pretrain_sw]
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self.attentions = None
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self.init_temperature = 10
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self.final_temperature_step = 10000
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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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x1, a1 = self.sw1(x, True)
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x2, a2 = self.sw2(x1, True)
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x_fea = self.feature_lr_conv(x2)
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x_fea = self.model_upsampler(x_fea)
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x_fea = self.feature_hr_conv1(x_fea)
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x_fea = self.feature_hr_conv2(x_fea)
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x_b_fea = self.b_fea_conv(x_grad)
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x_grad, a3 = self.sw_grad(x_b_fea, att_in=x1, output_attention_weights=True)
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x_grad = self.grad_lr_conv(x_grad)
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x_grad = self.branch_upsample(x_grad)
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x_out_branch = self.grad_branch_output_conv(x_grad)
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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
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def set_temperature(self, temp):
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[sw.set_temperature(temp) for sw in self.switches]
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def update_for_step(self, step, experiments_path='.'):
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if self.attentions:
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temp = max(1, 1 + self.init_temperature *
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(self.final_temperature_step - step) / self.final_temperature_step)
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self.set_temperature(temp)
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if step % 200 == 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
|
||||
|
||||
|
||||
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]
|
||||
|
||||
def update_for_step(self, step, experiments_path='.'):
|
||||
if self.attentions:
|
||||
temp = max(1, 1 + self.init_temperature *
|
||||
(self.final_temperature_step - step) / self.final_temperature_step)
|
||||
self.set_temperature(temp)
|
||||
if step % 200 == 0:
|
||||
output_path = os.path.join(experiments_path, "attention_maps", "a%i")
|
||||
prefix = "attention_map_%i_%%i.png" % (step,)
|
||||
[save_attention_to_image_rgb(output_path % (i,), self.attentions[i], self.transformation_counts, prefix, step) for i in range(len(self.attentions))]
|
||||
|
||||
def get_debug_values(self, step):
|
||||
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 SwitchedSpsrLr2(nn.Module):
|
||||
def __init__(self, in_nc, out_nc, nf, upscale=4):
|
||||
super(SwitchedSpsrLr2, self).__init__()
|
||||
n_upscale = int(math.log(upscale, 2))
|
||||
|
||||
# switch options
|
||||
transformation_filters = nf
|
||||
switch_filters = nf
|
||||
|
|
@ -708,7 +267,8 @@ class SwitchedSpsrLr2(nn.Module):
|
|||
# Grad branch
|
||||
self.get_g_nopadding = ImageGradientNoPadding()
|
||||
self.b_fea_conv = ConvGnLelu(in_nc, nf, kernel_size=3, norm=False, activation=False, bias=False)
|
||||
mplex_grad = functools.partial(ConvBasisMultiplexer, nf * 2, nf * 2, switch_reductions,
|
||||
self.sw_grad_mplex_converge = ConjoinBlock2(nf)
|
||||
mplex_grad = functools.partial(ConvBasisMultiplexer, nf, nf, switch_reductions,
|
||||
switch_processing_layers, self.transformation_counts // 2, use_exp2=True)
|
||||
self.sw_grad = ConfigurableSwitchComputer(transformation_filters, mplex_grad,
|
||||
pre_transform_block=pretransform_fn, transform_block=transform_fn,
|
||||
|
|
@ -752,7 +312,8 @@ class SwitchedSpsrLr2(nn.Module):
|
|||
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=torch.cat([x1, x_b_fea], dim=1), output_attention_weights=True)
|
||||
grad_mplex_in = self.sw_grad_mplex_converge(x1, passthrough=x_b_fea)
|
||||
x_grad, a3 = self.sw_grad(x_b_fea, att_in=grad_mplex_in, output_attention_weights=True)
|
||||
x_grad = self.grad_lr_conv(x_grad)
|
||||
x_grad = self.grad_hr_conv(x_grad)
|
||||
x_out_branch = self.upsample_grad(x_grad)
|
||||
|
|
|
|||
|
|
@ -433,6 +433,21 @@ class ConjoinBlock(nn.Module):
|
|||
return self.process(x)
|
||||
|
||||
|
||||
# Similar to ExpansionBlock2 but does not upsample.
|
||||
class ConjoinBlock2(nn.Module):
|
||||
def __init__(self, filters_in, filters_out=None, block=ConvGnSilu, norm=True):
|
||||
super(ConjoinBlock2, self).__init__()
|
||||
if filters_out is None:
|
||||
filters_out = filters_in
|
||||
self.process = block(filters_in*2, filters_in*2, kernel_size=3, bias=False, activation=True, norm=norm)
|
||||
self.decimate = block(filters_in*2, filters_out, kernel_size=1, bias=False, activation=False, norm=norm)
|
||||
|
||||
def forward(self, input, passthrough):
|
||||
x = torch.cat([input, passthrough], dim=1)
|
||||
x = self.process(x)
|
||||
return self.decimate(x)
|
||||
|
||||
|
||||
# Basic convolutional upsampling block that uses interpolate.
|
||||
class UpconvBlock(nn.Module):
|
||||
def __init__(self, filters_in, filters_out=None, block=ConvGnSilu, norm=True, activation=True, bias=False):
|
||||
|
|
|
|||
|
|
@ -113,10 +113,6 @@ def define_G(opt, net_key='network_G'):
|
|||
nb=opt_net['nb'], upscale=opt_net['scale'])
|
||||
elif which_model == "spsr_switched":
|
||||
netG = spsr.SwitchedSpsr(in_nc=3, out_nc=3, nf=opt_net['nf'], upscale=opt_net['scale'])
|
||||
elif which_model == "spsr_switched_lr":
|
||||
netG = spsr.SwitchedSpsrLr(in_nc=3, out_nc=3, nf=opt_net['nf'], upscale=opt_net['scale'])
|
||||
elif which_model == "spsr_switched_lr2":
|
||||
netG = spsr.SwitchedSpsrLr2(in_nc=3, out_nc=3, nf=opt_net['nf'], upscale=opt_net['scale'])
|
||||
|
||||
# image corruption
|
||||
elif which_model == 'HighToLowResNet':
|
||||
|
|
|
|||
|
|
@ -161,7 +161,7 @@ def main():
|
|||
current_step = resume_state['iter']
|
||||
model.resume_training(resume_state) # handle optimizers and schedulers
|
||||
else:
|
||||
current_step = 0
|
||||
current_step = -1
|
||||
start_epoch = 0
|
||||
|
||||
#### training
|
||||
|
|
|
|||
Loading…
Reference in New Issue
Block a user