133 lines
4.9 KiB
Python
133 lines
4.9 KiB
Python
import functools
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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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import models.archs.srflow.module_util as mutil
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from utils.util import opt_get
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class ResidualDenseBlock_5C(nn.Module):
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def __init__(self, nf=64, gc=32, bias=True):
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super(ResidualDenseBlock_5C, self).__init__()
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# gc: growth channel, i.e. intermediate channels
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self.conv1 = nn.Conv2d(nf, gc, 3, 1, 1, bias=bias)
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self.conv2 = nn.Conv2d(nf + gc, gc, 3, 1, 1, bias=bias)
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self.conv3 = nn.Conv2d(nf + 2 * gc, gc, 3, 1, 1, bias=bias)
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self.conv4 = nn.Conv2d(nf + 3 * gc, gc, 3, 1, 1, bias=bias)
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self.conv5 = nn.Conv2d(nf + 4 * gc, nf, 3, 1, 1, bias=bias)
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self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
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# initialization
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mutil.initialize_weights([self.conv1, self.conv2, self.conv3, self.conv4, self.conv5], 0.1)
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def forward(self, x):
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x1 = self.lrelu(self.conv1(x))
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x2 = self.lrelu(self.conv2(torch.cat((x, x1), 1)))
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x3 = self.lrelu(self.conv3(torch.cat((x, x1, x2), 1)))
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x4 = self.lrelu(self.conv4(torch.cat((x, x1, x2, x3), 1)))
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x5 = self.conv5(torch.cat((x, x1, x2, x3, x4), 1))
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return x5 * 0.2 + x
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class RRDB(nn.Module):
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'''Residual in Residual Dense Block'''
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def __init__(self, nf, gc=32):
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super(RRDB, self).__init__()
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self.RDB1 = ResidualDenseBlock_5C(nf, gc)
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self.RDB2 = ResidualDenseBlock_5C(nf, gc)
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self.RDB3 = ResidualDenseBlock_5C(nf, gc)
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def forward(self, x):
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out = self.RDB1(x)
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out = self.RDB2(out)
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out = self.RDB3(out)
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return out * 0.2 + x
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class RRDBNet(nn.Module):
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def __init__(self, in_nc, out_nc, nf, nb, gc=32, scale=4, opt=None):
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self.opt = opt
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super(RRDBNet, self).__init__()
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RRDB_block_f = functools.partial(RRDB, nf=nf, gc=gc)
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self.scale = scale
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self.conv_first = nn.Conv2d(in_nc, nf, 3, 1, 1, bias=True)
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self.RRDB_trunk = mutil.make_layer(RRDB_block_f, nb)
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self.trunk_conv = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
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#### upsampling
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self.upconv1 = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
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self.upconv2 = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
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if self.scale >= 8:
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self.upconv3 = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
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if self.scale >= 16:
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self.upconv4 = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
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if self.scale >= 32:
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self.upconv5 = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
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self.HRconv = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
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self.conv_last = nn.Conv2d(nf, out_nc, 3, 1, 1, bias=True)
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self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
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def forward(self, x, get_steps=False):
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fea = self.conv_first(x)
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block_idxs = opt_get(self.opt, ['network_G', 'flow', 'stackRRDB', 'blocks']) or []
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block_results = {}
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for idx, m in enumerate(self.RRDB_trunk.children()):
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fea = m(fea)
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for b in block_idxs:
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if b == idx:
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block_results["block_{}".format(idx)] = fea
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trunk = self.trunk_conv(fea)
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last_lr_fea = fea + trunk
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fea_up2 = self.upconv1(F.interpolate(last_lr_fea, scale_factor=2, mode='nearest'))
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fea = self.lrelu(fea_up2)
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fea_up4 = self.upconv2(F.interpolate(fea, scale_factor=2, mode='nearest'))
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fea = self.lrelu(fea_up4)
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fea_up8 = None
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fea_up16 = None
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fea_up32 = None
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if self.scale >= 8:
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fea_up8 = self.upconv3(F.interpolate(fea, scale_factor=2, mode='nearest'))
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fea = self.lrelu(fea_up8)
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if self.scale >= 16:
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fea_up16 = self.upconv4(F.interpolate(fea, scale_factor=2, mode='nearest'))
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fea = self.lrelu(fea_up16)
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if self.scale >= 32:
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fea_up32 = self.upconv5(F.interpolate(fea, scale_factor=2, mode='nearest'))
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fea = self.lrelu(fea_up32)
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out = self.conv_last(self.lrelu(self.HRconv(fea)))
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results = {'last_lr_fea': last_lr_fea,
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'fea_up1': last_lr_fea,
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'fea_up2': fea_up2,
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'fea_up4': fea_up4,
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'fea_up8': fea_up8,
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'fea_up16': fea_up16,
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'fea_up32': fea_up32,
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'out': out}
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fea_up0_en = opt_get(self.opt, ['network_G', 'flow', 'fea_up0']) or False
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if fea_up0_en:
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results['fea_up0'] = F.interpolate(last_lr_fea, scale_factor=1/2, mode='bilinear', align_corners=False, recompute_scale_factor=True)
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fea_upn1_en = opt_get(self.opt, ['network_G', 'flow', 'fea_up-1']) or False
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if fea_upn1_en:
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results['fea_up-1'] = F.interpolate(last_lr_fea, scale_factor=1/4, mode='bilinear', align_corners=False, recompute_scale_factor=True)
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if get_steps:
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for k, v in block_results.items():
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results[k] = v
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return results
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else:
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return out
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