def __init__(self, args):
super(EDSR, self).__init__()
nResBlock = args.nResBlock
nFeat = args.nFeat
scale = args.scale[0]
self.args = args
# Submean layer
self.subMean = common.meanShift(
args.rgbRange,
(0.4488, 0.4371, 0.4040), -1 * args.subMean)
# Head convolution for feature extracting
self.headConv = common.conv3x3(args.nChannel, nFeat)
# Main branch
modules = [common.ResBlock(nFeat) for _ in range(nResBlock)]
modules.append(common.conv3x3(nFeat, nFeat))
self.body = nn.Sequential(*modules)
# Upsampler
self.upsample = common.upsampler(scale, nFeat)
# Tail convolution for reconstruction
self.tailConv = common.conv3x3(nFeat, args.nChannel)
# Addmean layer
self.addMean = common.meanShift(
args.rgbRange,
(0.4488, 0.4371, 0.4040), 1 * args.subMean)
def __init__(self, args):
super(EDSR_scale, self).__init__(args)
nResBlock = args.nResBlock
nFeat = args.nFeat
# Main branch
modules = [
common.ResBlock_scale(nFeat, scale=0.1) for _ in range(nResBlock)]
modules.append(common.conv3x3(nFeat, nFeat))
self.body = nn.Sequential(*modules)
def __init__(self, args):
super(MDSR, self).__init__()
nResBlock = args.nResBlock
nFeat = args.nFeat
self.args = args
subMul, addMul = -1 * args.subMean, 1 * args.subMean
# Submean layer
self.subMean = common.meanShift(
args.rgbRange,
(0.4488, 0.4371, 0.4040),
subMul)
# Head convolution for feature extracting
self.headConv = common.conv3x3(args.nChannel, nFeat)
# Scale-dependent pre-processing module
self.preProcess = nn.ModuleList([
nn.Sequential(
common.ResBlock(nFeat, kernel_size=5),
common.ResBlock(nFeat, kernel_size=5)) for _ in args.scale])
# Main branch
modules = [common.ResBlock(nFeat) for _ in range(nResBlock)]
modules.append(common.conv3x3(nFeat, nFeat))
self.body = nn.Sequential(*modules)
# Scale-dependent upsampler
self.upsample = nn.ModuleList([
common.upsampler(s, nFeat) for s in args.scale])
# Tail convolution for reconstruction
self.tailConv = common.conv3x3(nFeat, args.nChannel)
# Addmean layer
self.addMean = common.meanShift(
args.rgbRange,
(0.4488, 0.4371, 0.4040),
addMul)
self.scaleIdx = 0