forked from mrq/DL-Art-School
Exchange SpsrNet for SpsrNetSimplified
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James Betker
parent
66d4512029
commit
e3053e4e55
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@ -86,150 +86,107 @@ class ImageGradientNoPadding(nn.Module):
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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', bl_inc=5):
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super(SPSRNet, self).__init__()
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self.bl_inc = bl_inc
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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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if upscale == 3:
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n_upscale = 1
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fea_conv = B.conv_block(in_nc + 1, 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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# 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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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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# 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 = B.conv_block(nf*2, nf, kernel_size=3, norm_type=None, act_type=None)
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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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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: torch.Tensor):
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# The attention_maps debugger outputs <x>. Save that here.
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self.lr = x.detach().cpu()
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def forward(self, x):
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x_grad = self.get_g_nopadding(x)
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b, f, w, h = x.shape
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x = torch.cat([x, torch.randn(b, 1, w, h, device=x.get_device())], dim=1)
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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 = self.model_fea_conv(x)
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x_ori = x
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for i in range(self.bl_inc):
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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 = self.model_shortcut_blk[i](x)
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x_fea1 = x
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for i in range(self.bl_inc):
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x = block_list[i+self.bl_inc](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(self.bl_inc):
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x = block_list[i+self.bl_inc*2](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(self.bl_inc):
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x = block_list[i+self.bl_inc*3](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 = block_list[self.bl_inc*4:](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 = 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_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_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_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_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_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_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_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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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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#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_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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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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@ -237,11 +194,10 @@ class SPSRNet(nn.Module):
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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 Spsr5(nn.Module):
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def __init__(self, in_nc, out_nc, nf, xforms=8, upscale=4, multiplexer_reductions=2, init_temperature=10):
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super(Spsr5, self).__init__()
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