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Add residual blocks to pyramid disc

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This commit is contained in:
James Betker 2020-11-11 13:56:45 -07:00
parent b4136d766a
commit 12b57bbd03
2 changed files with 32 additions and 3 deletions
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24
codes/models/archs/arch_util.py
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@ -139,6 +139,30 @@ class ResidualBlock_noBN(nn.Module):
return identity + out return identity + out
class ResidualBlockGN(nn.Module):
'''Residual block with GroupNorm
---Conv-GN-ReLU-Conv-+-
|________________|
'''
def __init__(self, nf=64):
super(ResidualBlockGN, self).__init__()
self.lrelu = nn.LeakyReLU(negative_slope=0.1, inplace=True)
self.conv1 = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
self.BN1 = nn.GroupNorm(8, nf)
self.conv2 = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
self.BN2 = nn.GroupNorm(8, nf)
# initialization
initialize_weights([self.conv1, self.conv2], 0.1)
def forward(self, x):
identity = x
out = self.lrelu(self.BN1(self.conv1(x)))
out = self.BN2(self.conv2(out))
return identity + out
def flow_warp(x, flow, interp_mode='bilinear', padding_mode='zeros'): def flow_warp(x, flow, interp_mode='bilinear', padding_mode='zeros'):
"""Warp an image or feature map with optical flow """Warp an image or feature map with optical flow
Args: Args:

11
codes/models/archs/discriminator_vgg_arch.py
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@ -2,7 +2,7 @@ import torch
import torch.nn as nn import torch.nn as nn
from models.archs.RRDBNet_arch import RRDB, RRDBWithBypass from models.archs.RRDBNet_arch import RRDB, RRDBWithBypass
from models.archs.arch_util import ConvBnLelu, ConvGnLelu, ExpansionBlock, ConvGnSilu from models.archs.arch_util import ConvBnLelu, ConvGnLelu, ExpansionBlock, ConvGnSilu, ResidualBlockGN
import torch.nn.functional as F import torch.nn.functional as F
from models.archs.SwitchedResidualGenerator_arch import gather_2d from models.archs.SwitchedResidualGenerator_arch import gather_2d
from models.archs.pyramid_arch import Pyramid from models.archs.pyramid_arch import Pyramid
@ -666,10 +666,15 @@ class PyramidDiscriminator(nn.Module):
def __init__(self, in_nc, nf, block=ConvGnLelu): def __init__(self, in_nc, nf, block=ConvGnLelu):
super(PyramidDiscriminator, self).__init__() super(PyramidDiscriminator, self).__init__()
self.initial_conv = block(in_nc, nf, kernel_size=3, stride=2, bias=True, norm=False, activation=True) self.initial_conv = block(in_nc, nf, kernel_size=3, stride=2, bias=True, norm=False, activation=True)
self.top_proc = nn.Sequential(*[ConvGnLelu(nf, nf, kernel_size=3, stride=2, bias=False, norm=True, activation=True)]) self.top_proc = nn.Sequential(*[ResidualBlockGN(nf),
ResidualBlockGN(nf),
ResidualBlockGN(nf)])
self.pyramid = Pyramid(nf, depth=3, processing_convs_per_layer=2, processing_at_point=2, self.pyramid = Pyramid(nf, depth=3, processing_convs_per_layer=2, processing_at_point=2,
scale_per_level=1.5, norm=True, return_outlevels=False) scale_per_level=1.5, norm=True, return_outlevels=False)
self.bottom_proc = nn.Sequential(*[ self.bottom_proc = nn.Sequential(*[ResidualBlockGN(nf),
ResidualBlockGN(nf),
ResidualBlockGN(nf),
ResidualBlockGN(nf),
ConvGnLelu(nf, nf // 2, kernel_size=1, activation=True, norm=True, bias=True), ConvGnLelu(nf, nf // 2, kernel_size=1, activation=True, norm=True, bias=True),
ConvGnLelu(nf // 2, nf // 4, kernel_size=1, activation=True, norm=True, bias=True), ConvGnLelu(nf // 2, nf // 4, kernel_size=1, activation=True, norm=True, bias=True),
ConvGnLelu(nf // 4, 1, activation=False, norm=False, bias=True)]) ConvGnLelu(nf // 4, 1, activation=False, norm=False, bias=True)])