Revert RRDB back to original model
This commit is contained in:
James Betker
parent
1ce863849a
commit
231137ab0a
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@ -1,293 +1,145 @@
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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.arch_util as arch_util
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from models.archs.arch_util import PixelUnshuffle
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import torchvision
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from utils.util import checkpoint
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from torch.utils.checkpoint import checkpoint_sequential
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from models.archs.arch_util import make_layer, default_init_weights
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class ResidualDenseBlock_5C(nn.Module):
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def __init__(self, nf=64, gc=32, bias=True, late_stage_kernel_size=3, late_stage_padding=1):
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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, late_stage_kernel_size, 1, late_stage_padding, bias=bias)
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self.conv4 = nn.Conv2d(nf + 3 * gc, gc, late_stage_kernel_size, 1, late_stage_padding, bias=bias)
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self.conv5 = nn.Conv2d(nf + 4 * gc, nf, late_stage_kernel_size, 1, late_stage_padding, bias=bias)
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class ResidualDenseBlock(nn.Module):
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"""Residual Dense Block.
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Used in RRDB block in ESRGAN.
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Args:
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mid_channels (int): Channel number of intermediate features.
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growth_channels (int): Channels for each growth.
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"""
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def __init__(self, mid_channels=64, growth_channels=32):
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super(ResidualDenseBlock, self).__init__()
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for i in range(5):
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out_channels = mid_channels if i == 4 else growth_channels
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self.add_module(
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f'conv{i+1}',
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nn.Conv2d(mid_channels + i * growth_channels, out_channels, 3,
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1, 1))
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self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
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for i in range(5):
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default_init_weights(getattr(self, f'conv{i+1}'), 0.1)
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# initialization
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arch_util.initialize_weights([self.conv1, self.conv2, self.conv3, self.conv4, self.conv5],
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0.1)
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def forward(self, x):
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"""Forward function.
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Args:
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x (Tensor): Input tensor with shape (n, c, h, w).
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Returns:
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Tensor: Forward results.
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"""
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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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# Emperically, we use 0.2 to scale the residual for better performance
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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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"""Residual in Residual Dense Block.
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def __init__(self, nf, gc=32):
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Used in RRDB-Net in ESRGAN.
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Args:
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mid_channels (int): Channel number of intermediate features.
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growth_channels (int): Channels for each growth.
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"""
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def __init__(self, mid_channels, growth_channels=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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self.rdb1 = ResidualDenseBlock(mid_channels, growth_channels)
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self.rdb2 = ResidualDenseBlock(mid_channels, growth_channels)
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self.rdb3 = ResidualDenseBlock(mid_channels, growth_channels)
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def forward(self, x):
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out = checkpoint(self.RDB1, x)
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out = checkpoint(self.RDB2, out)
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out = checkpoint(self.RDB3, out)
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"""Forward function.
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Args:
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x (Tensor): Input tensor with shape (n, c, h, w).
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Returns:
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Tensor: Forward results.
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"""
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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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# Emperically, we use 0.2 to scale the residual for better performance
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return out * 0.2 + x
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class LowDimRRDB(RRDB):
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def __init__(self, nf, gc=32, dimensional_adjustment=4):
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super(LowDimRRDB, self).__init__(nf * (dimensional_adjustment ** 2), gc * (dimensional_adjustment ** 2))
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self.unshuffle = PixelUnshuffle(dimensional_adjustment)
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self.shuffle = nn.PixelShuffle(dimensional_adjustment)
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class RRDBNet(nn.Module):
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"""Networks consisting of Residual in Residual Dense Block, which is used
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in ESRGAN.
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def forward(self, x):
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x = self.unshuffle(x)
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x = super(LowDimRRDB, self).forward(x)
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return self.shuffle(x)
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ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks.
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Currently, it supports x4 upsampling scale factor.
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Args:
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in_channels (int): Channel number of inputs.
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out_channels (int): Channel number of outputs.
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mid_channels (int): Channel number of intermediate features.
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Default: 64
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num_blocks (int): Block number in the trunk network. Defaults: 23
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growth_channels (int): Channels for each growth. Default: 32.
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"""
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# Identical to LowDimRRDB but wraps an RRDB rather than inheriting from it. TODO: remove LowDimRRDB when backwards
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# compatibility is no longer desired.
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class LowDimRRDBWrapper(nn.Module):
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# Do not specify nf or gc on the partial_rrdb passed in. That will be done by the wrapper.
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def __init__(self, nf, partial_rrdb, gc=32, dimensional_adjustment=4):
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super(LowDimRRDBWrapper, self).__init__()
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self.rrdb = partial_rrdb(nf=nf * (dimensional_adjustment ** 2), gc=gc * (dimensional_adjustment ** 2))
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self.unshuffle = PixelUnshuffle(dimensional_adjustment)
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self.shuffle = nn.PixelShuffle(dimensional_adjustment)
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def forward(self, x):
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x = self.unshuffle(x)
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x = self.rrdb(x)
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return self.shuffle(x)
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# This module performs the majority of the processing done by RRDBNet. It just doesn't have the upsampling at the end.
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class RRDBTrunk(nn.Module):
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def __init__(self, nf_in, nf_out, nb, gc=32, initial_stride=1, rrdb_block_f=None, conv_first_block=None):
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super(RRDBTrunk, self).__init__()
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if rrdb_block_f is None:
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rrdb_block_f = functools.partial(RRDB, nf=nf_out, gc=gc)
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if conv_first_block is None:
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self.conv_first = nn.Conv2d(nf_in, nf_out, 7, initial_stride, padding=3, bias=True)
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else:
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self.conv_first = conv_first_block
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self.RRDB_trunk, self.rrdb_layers = arch_util.make_layer(rrdb_block_f, nb, True)
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self.trunk_conv = nn.Conv2d(nf_out, nf_out, 3, 1, 1, bias=True)
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# Sets the softmax temperature of each RRDB layer. Only works if you are using attentive
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# convolutions.
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def set_temperature(self, temp):
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for layer in self.rrdb_layers:
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layer.set_temperature(temp)
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def forward(self, x):
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fea = self.conv_first(x)
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trunk = self.trunk_conv(self.RRDB_trunk(fea))
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fea = fea + trunk
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return fea
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# Adds some base methods that all RRDB* classes will use.
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class RRDBBase(nn.Module):
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def __init__(self):
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super(RRDBBase, self).__init__()
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# Sets the softmax temperature of each RRDB layer. Only works if you are using attentive
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# convolutions.
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def set_temperature(self, temp):
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for trunk in self.trunks:
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for layer in trunk.rrdb_layers:
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layer.set_temperature(temp)
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# This class uses a RRDBTrunk to perform processing on an image, then upsamples it.
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class RRDBNet(RRDBBase):
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def __init__(self, in_nc, out_nc, nf, nb, gc=32, scale=2, initial_stride=1,
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rrdb_block_f=None):
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def __init__(self,
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in_channels,
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out_channels,
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mid_channels=64,
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num_blocks=23,
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growth_channels=32):
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super(RRDBNet, self).__init__()
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# Trunk - does actual processing.
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self.trunk = RRDBTrunk(in_nc, nf, nb, gc, initial_stride, rrdb_block_f)
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self.trunks = [self.trunk]
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# Upsampling
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self.scale = scale
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self.upconv1 = nn.Conv2d(nf, nf, 5, 1, padding=2, bias=True)
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self.upconv2 = nn.Conv2d(nf, nf, 5, 1, padding=2, bias=True)
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self.HRconv = nn.Conv2d(nf, nf, 5, 1, padding=2, bias=True)
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self.conv_last = nn.Conv2d(nf, out_nc, 3, 1, 1, bias=True)
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self.conv_first = nn.Conv2d(in_channels, mid_channels, 3, 1, 1)
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self.body = make_layer(
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RRDB,
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num_blocks,
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mid_channels=mid_channels,
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growth_channels=growth_channels)
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self.conv_body = nn.Conv2d(mid_channels, mid_channels, 3, 1, 1)
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# upsample
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self.conv_up1 = nn.Conv2d(mid_channels, mid_channels, 3, 1, 1)
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self.conv_up2 = nn.Conv2d(mid_channels, mid_channels, 3, 1, 1)
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self.conv_hr = nn.Conv2d(mid_channels, mid_channels, 3, 1, 1)
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self.conv_last = nn.Conv2d(mid_channels, out_channels, 3, 1, 1)
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self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
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for m in [
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self.conv_first, self.conv_body, self.conv_up1,
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self.conv_up2, self.conv_hr, self.conv_last
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]:
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default_init_weights(m, 0.1)
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def forward(self, x):
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fea = self.trunk(x)
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"""Forward function.
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if self.scale >= 2:
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fea = F.interpolate(fea, scale_factor=2, mode='nearest')
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fea = self.lrelu(self.upconv1(fea))
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if self.scale >= 4:
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fea = F.interpolate(fea, scale_factor=2, mode='nearest')
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fea = self.lrelu(self.upconv2(fea))
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out = self.conv_last(self.lrelu(self.HRconv(fea)))
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Args:
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x (Tensor): Input tensor with shape (n, c, h, w).
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Returns:
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Tensor: Forward results.
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"""
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feat = self.conv_first(x)
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body_feat = self.conv_body(checkpoint_sequential(self.body, 5, feat))
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feat = feat + body_feat
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# upsample
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feat = self.lrelu(
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self.conv_up1(F.interpolate(feat, scale_factor=2, mode='nearest')))
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feat = self.lrelu(
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self.conv_up2(F.interpolate(feat, scale_factor=2, mode='nearest')))
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out = self.conv_last(self.lrelu(self.conv_hr(feat)))
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return out
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def load_state_dict(self, state_dict, strict=True):
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# The parameters in self.trunk used to be in this class. To support loading legacy saves, restore them.
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t_state = self.trunk.state_dict()
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for k in t_state.keys():
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if k in state_dict.keys():
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state_dict["trunk.%s" % (k,)] = state_dict.pop(k)
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super(RRDBNet, self).load_state_dict(state_dict, strict)
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# Variant of RRDBNet that is "assisted" by an external pretrained image classifier whose
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# intermediate layers have been splayed out, pixel-shuffled, and fed back in.
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# TODO: Convert to use new RRDBBase hierarchy.
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class AssistedRRDBNet(nn.Module):
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# in_nc=number of input channels.
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# out_nc=number of output channels.
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# nf=internal filter count
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# nb=number of additional blocks after the assistance layers.
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# gc=growth channel inside of residual blocks
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# scale=the number of times the output is doubled in size.
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# initial_stride=the stride on the first conv. can be used to downsample the image for processing.
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def __init__(self, in_nc, out_nc, nf, nb, gc=32, scale=2, initial_stride=1):
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super(AssistedRRDBNet, self).__init__()
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self.scale = scale
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self.conv_first = nn.Conv2d(in_nc, nf, 7, initial_stride, padding=3, bias=True)
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# Set-up the assist-net, which should do feature extraction for us.
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self.assistnet = torchvision.models.wide_resnet50_2(pretrained=True)
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self.set_enable_assistnet_training(False)
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assist_nf = [4, 8, 16] # Fixed for resnet. Re-evaluate if using other networks.
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self.assist2 = RRDB(nf + assist_nf[0], gc)
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self.assist3 = RRDB(nf + sum(assist_nf[:2]), gc)
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self.assist4 = RRDB(nf + sum(assist_nf), gc)
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nf = nf + sum(assist_nf)
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# After this, it's just a "standard" RRDB net.
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RRDB_block_f = functools.partial(RRDB, nf=nf, gc=gc)
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self.RRDB_trunk = arch_util.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, 5, 1, padding=2, bias=True)
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self.upconv2 = nn.Conv2d(nf, nf, 5, 1, padding=2, bias=True)
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self.HRconv = nn.Conv2d(nf, nf, 5, 1, padding=2, 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 set_enable_assistnet_training(self, en):
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for p in self.assistnet.parameters():
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p.requires_grad = en
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def res_extract(self, x):
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# Width and height must be factors of 16 to use this architecture. Check that here.
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(b, f, w, h) = x.shape
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assert w % 16 == 0
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assert h % 16 == 0
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x = self.assistnet.conv1(x)
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x = self.assistnet.bn1(x)
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x = self.assistnet.relu(x)
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x = self.assistnet.maxpool(x)
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x = self.assistnet.layer1(x)
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l1 = F.pixel_shuffle(x, 4)
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x = self.assistnet.layer2(x)
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l2 = F.pixel_shuffle(x, 8)
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x = self.assistnet.layer3(x)
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l3 = F.pixel_shuffle(x, 16)
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return l1, l2, l3
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def forward(self, x):
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# Invoke the assistant net first.
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l1, l2, l3 = self.res_extract(x)
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fea = self.conv_first(x)
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fea = self.assist2(torch.cat([fea, l3], dim=1))
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fea = self.assist3(torch.cat([fea, l2], dim=1))
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fea = self.assist4(torch.cat([fea, l1], dim=1))
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trunk = self.trunk_conv(self.RRDB_trunk(fea))
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fea = fea + trunk
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if self.scale >= 2:
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fea = F.interpolate(fea, scale_factor=2, mode='nearest')
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fea = self.lrelu(self.upconv1(fea))
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if self.scale >= 4:
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fea = F.interpolate(fea, scale_factor=2, mode='nearest')
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fea = self.lrelu(self.upconv2(fea))
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out = self.conv_last(self.lrelu(self.HRconv(fea)))
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return (out,)
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class PixShuffleInitialConv(nn.Module):
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def __init__(self, reduction_factor, nf_out):
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super(PixShuffleInitialConv, self).__init__()
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self.conv = nn.Conv2d(3 * (reduction_factor ** 2), nf_out, 1)
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self.unshuffle = PixelUnshuffle(reduction_factor)
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def forward(self, x):
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(b, f, w, h) = x.shape
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# This module can only be applied to input images (with 3 channels)
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assert f == 3
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x = self.unshuffle(x)
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return self.conv(x)
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# This class uses a RRDBTrunk to perform processing on an image, then upsamples it.
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class PixShuffleRRDB(RRDBBase):
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def __init__(self, nf, nb, gc=32, scale=2, rrdb_block_f=None):
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super(PixShuffleRRDB, self).__init__()
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# This class does a 4x pixel shuffle on the filter count inside the trunk, so nf must be divisible by 16.
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assert nf % 16 == 0
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# Trunk - does actual processing.
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self.trunk = RRDBTrunk(3, nf, nb, gc, 1, rrdb_block_f, PixShuffleInitialConv(4, nf))
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self.trunks = [self.trunk]
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# Upsampling
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pix_nf = int(nf/16)
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self.scale = scale
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self.upconv1 = nn.Conv2d(pix_nf, pix_nf, 5, 1, padding=2, bias=True)
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self.upconv2 = nn.Conv2d(pix_nf, pix_nf, 5, 1, padding=2, bias=True)
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self.HRconv = nn.Conv2d(pix_nf, pix_nf, 5, 1, padding=2, bias=True)
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self.conv_last = nn.Conv2d(pix_nf, 3, 3, 1, 1, bias=True)
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self.pixel_shuffle = nn.PixelShuffle(4)
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self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
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def forward(self, x):
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fea = self.trunk(x)
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fea = self.pixel_shuffle(fea)
|
||||
|
||||
if self.scale >= 2:
|
||||
fea = F.interpolate(fea, scale_factor=2, mode='nearest')
|
||||
fea = self.lrelu(self.upconv1(fea))
|
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if self.scale >= 4:
|
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fea = F.interpolate(fea, scale_factor=2, mode='nearest')
|
||||
fea = self.lrelu(self.upconv2(fea))
|
||||
out = self.conv_last(self.lrelu(self.HRconv(fea)))
|
||||
|
||||
return (out,)
|
||||
|
|
@ -5,6 +5,22 @@ import torch.nn.functional as F
|
|||
import torch.nn.utils.spectral_norm as SpectralNorm
|
||||
from math import sqrt
|
||||
|
||||
def kaiming_init(module,
|
||||
a=0,
|
||||
mode='fan_out',
|
||||
nonlinearity='relu',
|
||||
bias=0,
|
||||
distribution='normal'):
|
||||
assert distribution in ['uniform', 'normal']
|
||||
if distribution == 'uniform':
|
||||
nn.init.kaiming_uniform_(
|
||||
module.weight, a=a, mode=mode, nonlinearity=nonlinearity)
|
||||
else:
|
||||
nn.init.kaiming_normal_(
|
||||
module.weight, a=a, mode=mode, nonlinearity=nonlinearity)
|
||||
if hasattr(module, 'bias') and module.bias is not None:
|
||||
nn.init.constant_(module.bias, bias)
|
||||
|
||||
def pixel_norm(x, epsilon=1e-8):
|
||||
return x * torch.rsqrt(torch.mean(torch.pow(x, 2), dim=1, keepdims=True) + epsilon)
|
||||
|
||||
|
|
@ -28,14 +44,34 @@ def initialize_weights(net_l, scale=1):
|
|||
init.constant_(m.bias.data, 0.0)
|
||||
|
||||
|
||||
def make_layer(block, n_layers, return_layers=False):
|
||||
def make_layer(block, num_blocks, **kwarg):
|
||||
"""Make layers by stacking the same blocks.
|
||||
Args:
|
||||
block (nn.module): nn.module class for basic block.
|
||||
num_blocks (int): number of blocks.
|
||||
Returns:
|
||||
nn.Sequential: Stacked blocks in nn.Sequential.
|
||||
"""
|
||||
layers = []
|
||||
for _ in range(n_layers):
|
||||
layers.append(block())
|
||||
if return_layers:
|
||||
return nn.Sequential(*layers), layers
|
||||
else:
|
||||
return nn.Sequential(*layers)
|
||||
for _ in range(num_blocks):
|
||||
layers.append(block(**kwarg))
|
||||
return nn.Sequential(*layers)
|
||||
|
||||
|
||||
def default_init_weights(module, scale=1):
|
||||
"""Initialize network weights.
|
||||
Args:
|
||||
modules (nn.Module): Modules to be initialized.
|
||||
scale (float): Scale initialized weights, especially for residual
|
||||
blocks.
|
||||
"""
|
||||
for m in module.modules():
|
||||
if isinstance(m, nn.Conv2d):
|
||||
kaiming_init(m, a=0, mode='fan_in', bias=0)
|
||||
m.weight.data *= scale
|
||||
elif isinstance(m, nn.Linear):
|
||||
kaiming_init(m, a=0, mode='fan_in', bias=0)
|
||||
m.weight.data *= scale
|
||||
|
||||
|
||||
class ResidualBlock(nn.Module):
|
||||
|
|
|
|||
|
|
@ -110,6 +110,8 @@ class BaseModel():
|
|||
for k, v in load_net.items():
|
||||
if k.startswith('module.'):
|
||||
load_net_clean[k[7:]] = v
|
||||
if k.startswith('generator'): # Hack to fix ESRGAN pretrained model.
|
||||
load_net_clean[k[10:]] = v
|
||||
else:
|
||||
load_net_clean[k] = v
|
||||
network.load_state_dict(load_net_clean, strict=strict)
|
||||
|
|
|
|||
|
|
@ -36,14 +36,8 @@ def define_G(opt, net_key='network_G', scale=None):
|
|||
netG = SRResNet_arch.MSRResNet(in_nc=opt_net['in_nc'], out_nc=opt_net['out_nc'],
|
||||
nf=opt_net['nf'], nb=opt_net['nb'], upscale=opt_net['scale'])
|
||||
elif which_model == 'RRDBNet':
|
||||
# RRDB does scaling in two steps, so take the sqrt of the scale we actually want to achieve and feed it to RRDB.
|
||||
initial_stride = 1 if 'initial_stride' not in opt_net else opt_net['initial_stride']
|
||||
assert initial_stride == 1 or initial_stride == 2
|
||||
# Need to adjust the scale the generator sees by the stride since the stride causes a down-sample.
|
||||
gen_scale = scale * initial_stride
|
||||
netG = RRDBNet_arch.RRDBNet(in_nc=opt_net['in_nc'], out_nc=opt_net['out_nc'],
|
||||
nf=opt_net['nf'], nb=opt_net['nb'], scale=opt_net['scale'] if 'scale' in opt_net.keys() else gen_scale,
|
||||
initial_stride=initial_stride)
|
||||
netG = RRDBNet_arch.RRDBNet(in_channels=opt_net['in_nc'], out_channels=opt_net['out_nc'],
|
||||
mid_channels=opt_net['nf'], num_blocks=opt_net['nb'])
|
||||
elif which_model == 'rcan':
|
||||
#args: n_resgroups, n_resblocks, res_scale, reduction, scale, n_feats
|
||||
opt_net['rgb_range'] = 255
|
||||
|
|
|
|||
Loading…
Reference in New Issue
Block a user