Several new spsr nets
This commit is contained in:
James Betker
parent
3c0a2d6efe
commit
3ab39f0d22
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@ -4,7 +4,10 @@ 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, ExpansionBlock, UpconvBlock
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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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import functools
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class ImageGradient(nn.Module):
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@ -250,6 +253,7 @@ class SPSRNetSimplified(nn.Module):
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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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@ -336,3 +340,214 @@ class SPSRNetSimplified(nn.Module):
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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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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 = 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, nb, 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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trans_counts = 8
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multiplx_fn = functools.partial(ConvBasisMultiplexer, transformation_filters, switch_filters, switch_reductions,
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switch_processing_layers, trans_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=trans_layers,
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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=trans_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=trans_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=trans_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=trans_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, a4 = self._branch_pretrain_sw(x__branch_pretrain_cat, True)
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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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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 % 50 == 0:
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output_path = os.path.join(experiments_path, "attention_maps", "a%i")
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prefix = "attention_map_%i_%%i.png" % (step,)
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[save_attention_to_image_rgb(output_path % (i,), self.attentions[i], self.transformation_counts, prefix, step) for i in range(len(self.attentions))]
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def get_debug_values(self, step):
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temp = self.switches[0].switch.temperature
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mean_hists = [compute_attention_specificity(att, 2) for att in self.attentions]
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means = [i[0] for i in mean_hists]
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hists = [i[1].clone().detach().cpu().flatten() for i in mean_hists]
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val = {"switch_temperature": temp}
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for i in range(len(means)):
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val["switch_%i_specificity" % (i,)] = means[i]
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val["switch_%i_histogram" % (i,)] = hists[i]
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return val
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@ -134,7 +134,10 @@ class ConfigurableSwitchComputer(nn.Module):
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# depending on its needs.
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self.psc_scale = nn.Parameter(torch.full((1,), float(.1)))
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def forward(self, x, output_attention_weights=False, fixed_scale=1):
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def forward(self, x, output_attention_weights=False, att_in=None, fixed_scale=1):
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if att_in is None:
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att_in = x
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identity = x
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if self.add_noise:
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rand_feature = torch.randn_like(x) * self.noise_scale
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@ -143,7 +146,7 @@ class ConfigurableSwitchComputer(nn.Module):
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if self.pre_transform:
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x = self.pre_transform(x)
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xformed = [t.forward(x) for t in self.transforms]
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m = self.multiplexer(identity)
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m = self.multiplexer(att_in)
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outputs, attention = self.switch(xformed, m, True)
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@ -108,6 +108,9 @@ def define_G(opt, net_key='network_G'):
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elif which_model == 'spsr_net_improved':
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netG = spsr.SPSRNetSimplified(in_nc=opt_net['in_nc'], out_nc=opt_net['out_nc'], nf=opt_net['nf'],
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nb=opt_net['nb'], upscale=opt_net['scale'])
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elif which_model == 'spsr_net_improved_noskip':
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netG = spsr.SPSRNetSimplifiedNoSkip(in_nc=opt_net['in_nc'], out_nc=opt_net['out_nc'], nf=opt_net['nf'],
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nb=opt_net['nb'], upscale=opt_net['scale'])
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# image corruption
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elif which_model == 'HighToLowResNet':
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@ -32,7 +32,7 @@ def init_dist(backend='nccl', **kwargs):
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def main():
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#### options
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parser = argparse.ArgumentParser()
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parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/train_imgset_spsr_rrdb.yml')
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parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/train_imgset_spsr_rrdb_noskip.yml')
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parser.add_argument('--launcher', choices=['none', 'pytorch'], default='none',
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help='job launcher')
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parser.add_argument('--local_rank', type=int, default=0)
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22
sandbox.py
Normal file
22
sandbox.py
Normal file
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@ -0,0 +1,22 @@
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import torch
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import torchvision
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from PIL import Image
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def load_img(path):
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im = Image.open(path)
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return torchvision.transforms.ToTensor()(im)
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def save_img(t, path):
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torchvision.utils.save_image(t, path)
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img = load_img("me.png")
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# add zeros to the imaginary component
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img = torch.stack([img, torch.zeros_like(img)], dim=-1)
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fft = torch.fft(img, signal_ndim=2)
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fft_d = torch.zeros_like(fft)
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for i in range(-5, 5):
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diag = torch.diagonal(fft, offset=i, dim1=1, dim2=2)
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diag_em = torch.diag_embed(diag, offset=i, dim1=1, dim2=2)
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fft_d += diag_em
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resamp_img = torch.ifft(fft_d, signal_ndim=2)[:, :, :, 0]
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save_img(resamp_img, "resampled.png")
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