389 lines
16 KiB
Python
389 lines
16 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.arch_util as arch_util
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import torchvision
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import switched_conv as switched_conv
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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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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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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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# Multiple 5-channel residual block that uses learned switching to diversify its outputs.
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class SwitchedRDB_5C(switched_conv.MultiHeadSwitchedAbstractBlock):
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def __init__(self, nf=64, gc=32, num_convs=8, num_heads=2, init_temperature=1):
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rdb5c = functools.partial(ResidualDenseBlock_5C, nf, gc)
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super(SwitchedRDB_5C, self).__init__(
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rdb5c,
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nf,
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num_convs,
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num_heads,
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att_kernel_size=3,
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att_pads=1,
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initial_temperature=init_temperature,
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)
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self.mhead_collapse = nn.Conv2d(num_heads * nf, nf, 1)
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arch_util.initialize_weights([sw.attention_conv1 for sw in self.switches] +
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[sw.attention_conv2 for sw in self.switches] +
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[self.mhead_collapse], 1)
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def forward(self, x, output_attention_weights=False):
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outs = super(SwitchedRDB_5C, self).forward(x, output_attention_weights)
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if output_attention_weights:
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outs, atts = outs
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# outs need to be collapsed back down to a single heads worth of data.
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out = self.mhead_collapse(outs)
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return out, atts
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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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# RRDB block that uses switching on the individual RDB modules that compose it to increase learning diversity.
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class SwitchedRRDB(RRDB):
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def __init__(self, nf, gc=32, num_convs=8, init_temperature=1, final_temperature_step=1):
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super(RRDB, self).__init__()
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self.RDB1 = SwitchedRDB_5C(nf, gc, num_convs=num_convs, init_temperature=init_temperature)
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self.RDB2 = SwitchedRDB_5C(nf, gc, num_convs=num_convs, init_temperature=init_temperature)
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self.RDB3 = SwitchedRDB_5C(nf, gc, num_convs=num_convs, init_temperature=init_temperature)
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self.init_temperature = init_temperature
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self.final_temperature_step = final_temperature_step
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self.running_mean = 0
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self.running_count = 0
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def set_temperature(self, temp):
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[sw.set_attention_temperature(temp) for sw in self.RDB1.switches]
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[sw.set_attention_temperature(temp) for sw in self.RDB2.switches]
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[sw.set_attention_temperature(temp) for sw in self.RDB3.switches]
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def forward(self, x):
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out, att1 = self.RDB1(x, True)
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out, att2 = self.RDB2(out, True)
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out, att3 = self.RDB3(out, True)
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a1mean, _ = switched_conv.compute_attention_specificity(att1, 2)
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a2mean, _ = switched_conv.compute_attention_specificity(att2, 2)
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a3mean, _ = switched_conv.compute_attention_specificity(att3, 2)
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self.running_mean += (a1mean + a2mean + a3mean) / 3.0
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self.running_count += 1
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return out * 0.2 + x
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def get_debug_values(self, step, prefix):
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# Take the chance to update the temperature here.
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temp = max(1, int(self.init_temperature * (self.final_temperature_step - step) / self.final_temperature_step))
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self.set_temperature(temp)
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# Intentionally overwrite attention_temperature from other RRDB blocks; these should be synced.
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val = {"%s_attention_mean" % (prefix,): self.running_mean / self.running_count,
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"attention_temperature": temp}
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self.running_count = 0
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self.running_mean = 0
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return val
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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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def get_debug_values(self, step, prefix):
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val = {}
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i = 0
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for block in self.RRDB_trunk._modules.values():
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if hasattr(block, "get_debug_values"):
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val.update(block.get_debug_values(step, "%s_rdb_%i" % (prefix, i)))
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i += 1
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return val
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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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def get_debug_values(self, step):
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val = {}
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for i, trunk in enumerate(self.trunks):
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for j, block in enumerate(trunk.RRDB_trunk._modules.values()):
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if hasattr(block, "get_debug_values"):
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val.update(block.get_debug_values(step, "trunk_%i_block_%i" % (i, j)))
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return val
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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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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.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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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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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.r = 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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# Perform a "reverse-pixel-shuffle", reducing the image size and increasing filter count by self.r**2
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x = x.contiguous().view(b, 3, w // self.r, self.r, h // self.r, self.r)
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x = x.permute(0, 1, 3, 5, 2, 4).contiguous().view(b, 3 * (self.r ** 2), w // self.r, h // self.r)
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# Apply the conv to bring the filter account to the desired size.
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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)
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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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# This class uses two RRDB trunks to process an image at different resolution levels.
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class MultiRRDBNet(RRDBBase):
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def __init__(self, nf_base, gc_base, lo_blocks, hi_blocks, scale=2, rrdb_block_f=None):
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super(MultiRRDBNet, self).__init__()
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# Chained trunks
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lo_nf = nf_base * 4
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lo_nf_out = nf_base // 4
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hi_nf = nf_base
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self.lo_trunk = RRDBTrunk(nf_base, lo_nf, lo_blocks, gc_base * 2, initial_stride=1, rrdb_block_f=rrdb_block_f, conv_first_block=PixShuffleInitialConv(4, lo_nf))
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self.skip_conv = nn.Conv2d(3, lo_nf_out, 1)
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self.hi_trunk = RRDBTrunk(lo_nf_out, hi_nf, hi_blocks, gc_base, initial_stride=1, rrdb_block_f=rrdb_block_f)
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self.trunks = [self.lo_trunk, self.hi_trunk]
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# Upsampling
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self.scale = scale
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self.upconv1 = nn.Conv2d(hi_nf, hi_nf, 5, 1, padding=2, bias=True)
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self.upconv2 = nn.Conv2d(hi_nf, hi_nf, 5, 1, padding=2, bias=True)
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self.HRconv = nn.Conv2d(hi_nf, hi_nf, 5, 1, padding=2, bias=True)
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self.conv_last = nn.Conv2d(hi_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_lo = self.lo_trunk(x)
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fea = self.pixel_shuffle(fea_lo) + self.skip_conv(x)
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fea = self.hi_trunk(fea)
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# Upsampling.
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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,) |