def __init__(self, args, conv=common.default_conv):
super(RR, self).__init__()
n_resblocks = 16 # args.n_resblocks
n_feats = 64 # args.n_feats
kernel_size = 3
rgb_mean = (0.4488, 0.4371, 0.4040)
rgb_std = (1.0, 1.0, 1.0)
msa = CES(in_channels=n_feats,num=args.stages)#blocks=args.blocks)
# define head module
m_head = [conv(args.n_colors, n_feats, kernel_size)]
# define body module
m_body = [
common.ResBlock(
conv, n_feats, kernel_size, nn.PReLU(), res_scale=args.res_scale
) for _ in range(n_resblocks // 2)
]
m_body.append(msa)
for i in range(n_resblocks // 2):
m_body.append(common.ResBlock(conv, n_feats, kernel_size, nn.PReLU(), res_scale=args.res_scale))
m_body.append(conv(n_feats, n_feats, kernel_size))
m_tail = [
conv(n_feats, args.n_colors, kernel_size)
]
self.add_mean = common.MeanShift(args.rgb_range, rgb_mean, rgb_std, 1)
self.head = nn.Sequential(*m_head)
self.body = nn.Sequential(*m_body)
self.tail = nn.Sequential(*m_tail)
def __init__(self, args, conv=common.default_conv):
super(MDSR, self).__init__()
n_resblocks = args.n_resblocks
n_feats = args.n_feats
kernel_size = 3
act = nn.ReLU(True)
self.scale_idx = 0
self.url = url["r{}f{}".format(n_resblocks, n_feats)]
self.sub_mean = common.MeanShift(args.rgb_range)
self.add_mean = common.MeanShift(args.rgb_range, sign=1)
m_head = [conv(args.n_colors, n_feats, kernel_size)]
self.pre_process = nn.ModuleList([
nn.Sequential(
common.ResBlock(conv, n_feats, 5, act=act),
common.ResBlock(conv, n_feats, 5, act=act),
) for _ in args.scale
])
m_body = [
common.ResBlock(conv, n_feats, kernel_size, act=act)
for _ in range(n_resblocks)
]
m_body.append(conv(n_feats, n_feats, kernel_size))
self.upsample = nn.ModuleList([
common.Upsampler(conv, s, n_feats, act=False) for s in args.scale
])
m_tail = [conv(n_feats, args.n_colors, kernel_size)]
self.head = nn.Sequential(*m_head)
self.body = nn.Sequential(*m_body)
self.tail = nn.Sequential(*m_tail)
def __init__(self, args, n_feats, n_layer, kernel_size=3, conv=common.default_conv):
super(Edge_Net, self).__init__()
self.trans = conv(args.n_colors, n_feats, kernel_size)
self.head = common.ResBlock(conv, n_feats, kernel_size)
self.rdb = FEB(args, n_layer)
self.tail = common.ResBlock(conv, n_feats, kernel_size)
self.rebuilt = conv(n_feats, args.n_colors, kernel_size)
def __init__(self, args, conv=common.default_conv, gated_conv=gated_conv):
super(CGC_EDSR, self).__init__()
n_resblocks = args.n_resblocks
n_feats = args.n_feats
kernel_size = 3
scale = args.scale[0]
act = nn.ReLU(True)
url_name = 'cgc_edsr_r{}f{}x{}'.format(n_resblocks, n_feats, scale)
if url_name in url:
self.url = url[url_name]
else:
self.url = None
self.sub_mean = common.MeanShift(args.rgb_range)
self.add_mean = common.MeanShift(args.rgb_range, sign=1)
# define head module
m_head = [conv(args.n_colors, n_feats, kernel_size)]
# define body module
m_body = []
if not args.one_cgc_block:
m_body = [
common.ResBlock(gated_conv,
n_feats,
kernel_size,
act=act,
res_scale=args.res_scale)
for _ in range(n_resblocks)
]
m_body.append(gated_conv(n_feats, n_feats, kernel_size))
else:
for i in range(n_resblocks - 1):
m_body.append(
common.ResBlock(conv,
n_feats,
kernel_size,
act=act,
res_scale=args.res_scale))
m_body.append(
common.ResBlock(gated_conv,
n_feats,
kernel_size,
act=act,
res_scale=args.res_scale))
m_body.append(conv(n_feats, n_feats, kernel_size))
# define tail module
m_tail = [
common.Upsampler(conv, scale, n_feats, act=False),
conv(n_feats, args.n_colors, kernel_size)
]
self.head = nn.Sequential(*m_head)
self.body = nn.Sequential(*m_body)
self.tail = nn.Sequential(*m_tail)
def __init__(self, args, conv=common.default_conv):
super(EDSR, self).__init__()
self.fullTrain = args.fullTrain
self.fullInputScale = args.fullInputScale
self.fullTargetScale = args.fullTargetScale
n_resblock = args.n_resblocks
batch_norm = args.BN
n_feats = args.n_feats
kernel_size = 3
scale = args.scale[0]
act = nn.ReLU(True)
rgb_mean = (0.4488, 0.4371, 0.4040)
self.sub_mean = common.MeanShift(args.rgb_range, rgb_mean, -1)
# define head module
modules_head = [conv(args.n_colors, n_feats, kernel_size)]
modules_head_DNN = [conv(args.n_colors, n_feats, 7)]
modules_head_DNN.append(act)
# define body module
modules_body = [
common.ResBlock(
conv, n_feats, kernel_size, act=act, res_scale=args.res_scale) \
for _ in range(n_resblock)]
modules_body.append(conv(n_feats, n_feats, kernel_size))
modules_body_DNN = [
common.ResBlock(
conv, n_feats, kernel_size, act=act,bias = True,bn=batch_norm, res_scale=args.res_scale) \
for _ in range(n_resblock)]
modules_body.append(conv(n_feats, n_feats, kernel_size))
# define tail module
modules_tail = [
common.Upsampler(conv, 2, n_feats, act=False),
conv(n_feats, args.n_colors, kernel_size)
]
modules_tail_DNN = [conv(n_feats, args.n_colors, kernel_size)]
self.add_mean = common.MeanShift(args.rgb_range, rgb_mean, 1)
self.head_DNN = nn.Sequential(*modules_head_DNN)
self.body_DNN = nn.Sequential(*modules_body_DNN)
#self.tail_4 = nn.Sequential(*modules_tail)
self.tail_DNN = nn.Sequential(*modules_tail_DNN)
self.head_2 = nn.Sequential(*modules_head)
self.body_2 = nn.Sequential(*modules_body)
self.tail_2 = nn.Sequential(*modules_tail)
self.head = nn.Sequential(*modules_head)
self.body = nn.Sequential(*modules_body)
self.tail = nn.Sequential(*modules_tail)
def __init__(self, name="CSPDarkNet53", **kwargs):
super(CSPDarkNet53, self).__init__(self, name=name, **kwargs)
self.conv1_1 = cm.ConvBlock((3, 3, 3, 32), activate_type="mish")
self.conv1_2 = cm.ConvBlock((3, 3, 32, 64), downsample=True, activate_type="mish")
self.conv2_1 = cm.ConvBlock((1, 1, 64, 64), activate_type="mish")
self.conv2_2 = cm.ConvBlock((1, 1, 64, 64), activate_type="mish")
self.res_blocks1 = []
for _ in range(1):
self.res_blocks1.append(cm.ResBlock(64, 32, 64, activate_type="mish"))
self.conv2_3 = cm.ConvBlock((1, 1, 64, 64), activate_type="mish")
self.conv3_1 = cm.ConvBlock((1, 1, 128, 64), activate_type="mish")
self.conv3_2 = cm.ConvBlock((3, 3, 64, 128), downsample=True, activate_type="mish")
self.conv4_1 = cm.ConvBlock((1, 1, 128, 64), activate_type="mish")
self.conv4_2 = cm.ConvBlock((1, 1, 128, 64), activate_type="mish")
self.res_blocks2 = []
for _ in range(2):
self.res_blocks2.append(cm.ResBlock(64, 64, 64, activate_type="mish"))
self.conv4_3 = cm.ConvBlock((1, 1, 64, 64), activate_type="mish")
self.conv5_1 = cm.ConvBlock((1, 1, 128, 128), activate_type="mish")
self.conv5_2 = cm.ConvBlock((3, 3, 128, 256), downsample=True, activate_type="mish")
self.conv6_1 = cm.ConvBlock((1, 1, 256, 128), activate_type="mish")
self.conv6_2 = cm.ConvBlock((1, 1, 256, 128), activate_type="mish")
self.res_blocks3 = []
for _ in range(8):
self.res_blocks3.append(cm.ResBlock(128, 128, 128, activate_type="mish"))
self.conv6_3 = cm.ConvBlock((1, 1, 128, 128), activate_type="mish")
self.conv7_1 = cm.ConvBlock((1, 1, 256, 256), activate_type="mish")
self.conv7_2 = cm.ConvBlock((3, 3, 256, 512), downsample=True, activate_type="mish")
self.conv8_1 = cm.ConvBlock((1, 1, 512, 256), activate_type="mish")
self.conv8_2 = cm.ConvBlock((1, 1, 512, 256), activate_type="mish")
self.res_blocks4 = []
for _ in range(8):
self.res_blocks4.append(cm.ResBlock(256, 256, 256, activate_type="mish"))
self.conv8_3 = cm.ConvBlock((1, 1, 256, 256), activate_type="mish")
self.conv9_1 = cm.ConvBlock((1, 1, 512, 512), activate_type="mish")
self.conv9_2 = cm.ConvBlock((3, 3, 512, 1024), downsample=True, activate_type="mish")
self.conv10_1 = cm.ConvBlock((1, 1, 1024, 512), activate_type="mish")
self.conv10_2 = cm.ConvBlock((1, 1, 1024, 512), activate_type="mish")
self.res_blocks5 = []
for _ in range(4):
self.res_blocks5.append(cm.ResBlock(512, 512, 512, activate_type="mish"))
self.conv10_3 = cm.ConvBlock((1, 1, 512, 512), activate_type="mish")
self.conv_last = cm.ConvBlock((1, 1, 1024, 1024), activate_type="mish")
def __init__(self, args, conv=common.default_conv):
super(LDSR, self).__init__()
n_resblocks = args.n_resblocks
n_feats = args.n_feats
kernel_size = 3
scale = args.scale[0]
act = nn.ReLU(True)
self.url = url['r{}f{}x{}'.format(n_resblocks, n_feats, scale)]
self.sub_mean = common.MeanShift(args.rgb_range)
self.add_mean = common.MeanShift(args.rgb_range, sign=1)
# define head module
# HaiMai: Add 320 to fit Block35
inputs=320
outputs=384
m_head = [conv(args.n_colors, inputs, kernel_size)]
# define body module
# HaiMai: Add Block35 from inceptionV4ResV2
step = 4
#Input/ouput 320/320
m_body=[InceptionResNetV2.Block35Plus(inputs,kernel_size, act=act, res_scale=args.res_scale)]
# Input/Ouput 320/384 => 384/n_feats
m_body.append(InceptionResNetV2.Mixed_6aPlus(inputs,outputs))
m_body.append(conv(outputs,n_feats,kernel_size))
m_body.append([
common.ResBlock(
conv, n_feats, kernel_size, act=act, res_scale=args.res_scale
) for _ in range(n_resblocks/step)
])
for _ in range(step-1):
m_body.append(InceptionResNetV2.Block35Plus(inputs,kernel_size, act=act, res_scale=args.res_scale))
# Input/Ouput 320/384 => 384/n_feats
m_body.append(InceptionResNetV2.Mixed_6aPlus(inputs,outputs))
m_body.append(conv(outputs,n_feats,kernel_size))
m_body.append([common.ResBlock(
conv, n_feats, kernel_size, act=act, res_scale=args.res_scale
) for _ in range(n_resblocks/step)])
m_body.append(conv(n_feats, n_feats, kernel_size))
# define tail module
m_tail = [
common.Upsampler(conv, scale, n_feats, act=False),
conv(n_feats, args.n_colors, kernel_size)
]
self.head = nn.Sequential(*m_head)
self.body = nn.Sequential(*m_body)
self.tail = nn.Sequential(*m_tail)
def __init__(self, args):
super(Discriminator, self).__init__()
in_channels = args.n_colors
out_channels = 64
depth = 7
conv = common.default_conv
act = nn.LeakyReLU(True)
def _block(_in_channels, _out_channels, stride=1):
return nn.Sequential(
nn.Conv2d(_in_channels,
_out_channels,
3,
padding=1,
stride=stride,
bias=False), nn.BatchNorm2d(_out_channels),
nn.LeakyReLU(negative_slope=0.2, inplace=True))
m_features = [_block(in_channels, out_channels)]
for i in range(depth):
in_channels = out_channels
if i % 2 == 1:
stride = 1
out_channels *= 2
else:
stride = 2
m_features.append(_block(in_channels, out_channels, stride=stride))
m_features.append(
common.ResBlock(conv,
out_channels,
kernel_size=3,
bn=True,
act=act,
res_scale=1))
m_features.append(
common.ResBlock(conv,
out_channels,
kernel_size=3,
bn=True,
act=act,
res_scale=1))
patch_size = args.patch_size // (2**((depth + 1) // 2))
m_classifier = [
nn.Linear(out_channels * patch_size**2, 1024),
nn.LeakyReLU(negative_slope=0.2, inplace=True),
nn.Linear(1024, 1)
]
self.features = nn.Sequential(*m_features)
self.classifier = nn.Sequential(*m_classifier)
def __init__(self, conv=common.default_conv, **kwargs,):
super(MSMNetModel, self).__init__()
# -------------- Define multi-scale model architecture here ------------
# scale = 3
input_channles = kwargs['input_channels']
num_resblocks = kwargs['num_ms_resblocks']
intermediate_channels = kwargs['intermediate_channels']
kernel_size = kwargs['default_kernel_size']
activation = nn.ReLU(True)
rgb_range = kwargs['rgb_range']
self.sub_mean = common.MeanShift(rgb_range)
self.add_mean = common.MeanShift(rgb_range, sign=1)
# head to read scaled image
_head = common.BasicBlock(conv, input_channles, intermediate_channels, kernel_size)
# pre-process 2*Resblock each
self.pre_process = nn.ModuleList([
nn.Sequential(
common.PreResBlock(conv, 2*intermediate_channels, intermediate_channels, 5, bn=True, act=activation),
common.ResBlock(conv, intermediate_channels, 5, bn=True, act=activation)
),
nn.Sequential(
common.PreResBlock(conv, 2*intermediate_channels, intermediate_channels, 5, bn=True, act=activation),
common.ResBlock(conv, intermediate_channels, 5, bn=True, act=activation)
),
nn.Sequential(
common.ResBlock(conv, intermediate_channels, 5, bn=True, act=activation),
common.ResBlock(conv, intermediate_channels, 5, bn=True, act=activation)
)
])
# body 16*Resblocks each
_body = [
common.ResBlock(
conv, intermediate_channels, kernel_size, bn=True, act=activation
) for _ in range(num_resblocks)
]
_body.append(conv(intermediate_channels, intermediate_channels, kernel_size))
# upsample to enlarge the scale
self.upsample = common.Upsampler(conv, intermediate_channels, bn=False, act=False)
_output = nn.ModuleList([
# conv(3, 1, 3),
common.BasicBlock(conv, intermediate_channels, intermediate_channels, kernel_size, act=nn.Sigmoid())
])
self.head = nn.Sequential(*_head)
self.body = nn.Sequential(*_body)
self.output = nn.Sequential(*_output)
def __init__(self, args, conv=common.default_conv):
super(PAEDSR, self).__init__()
n_resblock = args.n_resblocks
n_feats = args.n_feats
kernel_size = 3
scale = args.scale[0]
act = nn.ReLU(True)
rgb_mean = (0.4488, 0.4371, 0.4040)
rgb_std = (1.0, 1.0, 1.0)
self.sub_mean = common.MeanShift(args.rgb_range, rgb_mean, rgb_std)
self.msa = attention.PyramidAttention(channel=256,
reduction=8,
res_scale=args.res_scale)
# define head module
m_head = [conv(args.n_colors, n_feats, kernel_size)]
# define body module
m_body = [
common.ResBlock(conv,
n_feats,
kernel_size,
act=act,
res_scale=args.res_scale)
for _ in range(n_resblock // 2)
]
m_body.append(self.msa)
for _ in range(n_resblock // 2):
m_body.append(
common.ResBlock(conv,
n_feats,
kernel_size,
act=act,
res_scale=args.res_scale))
m_body.append(conv(n_feats, n_feats, kernel_size))
# define tail module
m_tail = [
common.Upsampler(conv, scale, n_feats, act=False),
nn.Conv2d(n_feats,
args.n_colors,
kernel_size,
padding=(kernel_size // 2))
]
self.add_mean = common.MeanShift(args.rgb_range, rgb_mean, rgb_std, 1)
self.head = nn.Sequential(*m_head)
self.body = nn.Sequential(*m_body)
self.tail = nn.Sequential(*m_tail)
def __init__(self, n_resblock=24, n_feats=256, scale=2, bias=True, norm_type=False,
act_type='prelu'):
super(NET, self).__init__()
self.scale = scale
m = [common.default_conv(1, n_feats, 3, stride=2)]
m += [nn.PixelShuffle(2),
common.ConvBlock(n_feats//4, n_feats, bias=True, act_type=act_type)
]
m += [common.ResBlock(n_feats, 3, norm_type, act_type, res_scale=1, bias=bias)
for _ in range(n_resblock)]
for _ in range(int(math.log(scale, 2))):
m += [nn.PixelShuffle(2),
common.ConvBlock(n_feats//4, n_feats, bias=True, act_type=act_type)
]
m += [common.default_conv(n_feats, 3, 3)]
self.model = nn.Sequential(*m)
for m in self.modules():
# pdb.set_trace()
if isinstance(m, nn.Conv2d):
# Xavier
# nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
nn.init.xavier_normal_(m.weight)
m.weight.requires_grad = True
if m.bias is not None:
m.bias.data.zero_()
m.bias.requires_grad = True
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, conv=common.default_conv):
super(SRResNet, self).__init__()
n_resblock = args.n_resblocks
n_feats = args.n_feats
scale = args.scale[0]
kernel_size = 3
act = nn.PReLU()
head = [conv(args.n_colors, n_feats, kernel_size=9), act]
body = [
common.ResBlock(conv,
n_feats,
kernel_size,
bias=True,
bn=True,
act=act) for _ in range(n_resblock)
]
body.extend(
[conv(n_feats, n_feats, kernel_size),
nn.BatchNorm2d(n_feats)])
tail = [
common.Upsampler(conv, scale, n_feats, act=act),
conv(n_feats, args.n_colors, kernel_size)
]
self.head = nn.Sequential(*head)
self.body = nn.Sequential(*body)
self.tail = nn.Sequential(*tail)
def __init__(self, scale_list, model_path = 'weight/EDSR_weight.pt' ):
super(EDSR, self).__init__()
# args
scale = scale_list[0]
input_channel = 5
output_channel = 3
num_block = 16
inp = 64
rgb_range = 255
res_scale = 0.1
act = nn.ReLU(True)
#act = nn.LeakyReLU(negative_slope=0.05, inplace=True)
# head
self.head = nn.Sequential( common.conv(3, inp, input_channel) )
# body
self.body = nn.Sequential( *[ common.ResBlock(inp, bias = True, act = act, res_scale = res_scale) for _ in range( num_block) ] )
self.body.add_module( str(num_block), common.conv(inp, inp, 3) )
# tail
if scale > 1:
self.tail = nn.Sequential( *[ common.Upsampler(scale, inp, act = False, choice = 0),
common.conv(inp, 3, output_channel) ] )
else:
self.tail = nn.Sequential( *[ common.conv(inp, 3, output_channel) ] )
self.sub_mean = common.MeanShift(rgb_range, sign = -1)
self.add_mean = common.MeanShift(rgb_range, sign = 1)
self.model_path = model_path
self.load()
def __init__(self, args, conv=common.default_conv):
super(EDSR, self).__init__()
print("EDSR MODEL is being used")
n_resblock = args.n_resblocks
n_feats = args.n_feats
kernel_size = 3
scale = args.scale[0]
act = nn.ReLU(True)
rgb_mean = (0.4488, 0.4371, 0.4040)
rgb_std = (1.0, 1.0, 1.0)
self.sub_mean = common.MeanShift(args.rgb_range, rgb_mean, rgb_std)
# define head module
m_head = [conv(args.n_colors, n_feats, kernel_size)]
# define body module
m_body = [
common.ResBlock(conv,
n_feats,
kernel_size,
act=act,
res_scale=args.res_scale)
for _ in range(n_resblock)
]
m_body.append(conv(n_feats, n_feats, kernel_size))
m_tail = [conv(n_feats, args.n_colors, kernel_size)]
self.head = nn.Sequential(*m_head)
self.body = nn.Sequential(*m_body)
self.tail = nn.Sequential(*m_tail)
def __init__(self, conv=common.default_conv):
super(EDSR, self).__init__()
n_resblocks = 32
n_feats = 64
kernel_size = 3
scale = 2
act = 'relu'
#self.url = url['r{}f{}x{}'.format(n_resblocks, n_feats, scale)]
# define head module
m_head = [conv(3, n_feats, kernel_size)]
# define body module
m_body = [
common.ResBlock(n_feats,
kernel_size,
act_type=act,
bias=True,
res_scale=1) for _ in range(n_resblocks)
]
m_body.append(conv(n_feats, n_feats, kernel_size))
# define tail module
m_tail = [
common.Upsampler(scale, n_feats, norm_type=False, act_type=False),
conv(n_feats, 3, kernel_size)
]
self.sub_mean = ops.MeanShift((0.4488, 0.4371, 0.4040), sub=True)
self.add_mean = ops.MeanShift((0.4488, 0.4371, 0.4040), sub=False)
self.head = nn.Sequential(*m_head)
self.body = nn.Sequential(*m_body)
self.tail = nn.Sequential(*m_tail)
def __init__(self, args, conv=common.default_conv):
super(EDSR, self).__init__()
n_resblocks = args.n_resblocks
n_feats = args.n_feats
kernel_size = 3
scale = args.scale[0]
act = nn.ReLU(True)
# self.url = url['r{}f{}x{}'.format(n_resblocks, n_feats, scale)]
self.sub_mean = common.MeanShift(args.rgb_range)
self.add_mean = common.MeanShift(args.rgb_range, sign=1)
# define head module
m_head = [conv(args.n_colors, n_feats, kernel_size)]
# define body module
m_body = [
common.ResBlock(
conv, n_feats, kernel_size, act=act, res_scale=args.res_scale
) for _ in range(n_resblocks)
]
m_body.append(conv(n_feats, n_feats, kernel_size))
# define tail module
m_tail = [
common.Upsampler(conv, scale, n_feats, act=False),
conv(n_feats, args.n_colors, kernel_size)
]
self.head = nn.Sequential(*m_head)
self.body = nn.Sequential(*m_body)
self.tail = nn.Sequential(*m_tail)
def __init__(self, args, conv=common.default_conv):
super(EDSR, self).__init__()
n_resblocks = args.n_resblocks # 16
n_feats = args.n_feats # 64
kernel_size = 3
scale = args.scale[0] # 4
act = nn.ReLU(True)
self.url = url['r{}f{}x{}'.format(n_resblocks, n_feats, scale)]
self.sub_mean = common.MeanShift(args.rgb_range) # 标准化输入
self.add_mean = common.MeanShift(args.rgb_range, sign=1) # 还原输出
# define head module
m_head = [conv(args.n_colors, n_feats, kernel_size)] # channels：3->64
# define body module
m_body = [ # 16个resblock
common.ResBlock( # ## 参数：64, 3, relu, 1
conv,
n_feats,
kernel_size,
act=act,
res_scale=args.res_scale) for _ in range(n_resblocks)
]
m_body.append(conv(n_feats, n_feats, kernel_size)) # channels：64->64
# define tail module
m_tail = [
common.Upsampler(conv, scale, n_feats, act=False), # 上采样集中在这里
conv(n_feats, args.n_colors, kernel_size)
]
self.head = nn.Sequential(*m_head)
self.body = nn.Sequential(*m_body)
self.tail = nn.Sequential(*m_tail)
def __init__(self, args, conv=common.default_conv):
super(HRST_CNN, self).__init__()
n_resblocks = args.n_resblocks
args.n_resblocks = args.n_resblocks - args.n_resblocks_ft
n_feats = args.n_feats
kernel_size = 3
scale = args.scale[0]
act = nn.ReLU(True)
body_ft = [
common.ResBlock(conv,
n_feats,
kernel_size,
act=act,
res_scale=args.res_scale)
for _ in range(args.n_resblocks_ft)
]
body_ft.append(conv(n_feats, n_feats, kernel_size))
tail_ft = [
conv(n_feats, n_feats, kernel_size),
conv(n_feats, n_feats, kernel_size),
conv(n_feats, args.n_colors, kernel_size)
]
premodel = EDSR(args)
self.sub_mean = premodel.sub_mean
self.head = premodel.head
body = premodel.body
body_child = list(body.children())
body_child.pop()
self.body = nn.Sequential(*body_child)
self.body_ft = nn.Sequential(*body_ft)
self.tail_ft = nn.Sequential(*tail_ft)
self.add_mean = premodel.add_mean
args.n_resblocks = n_resblocks
def __init__(self, args, conv3x3=common.default_conv, conv1x1=common.default_conv):
super(SRRESNET_CLUSTER, self).__init__()
n_resblock = args.n_resblocks
n_feats = args.n_feats
scale = args.scale[0]
act_res_flag = args.act_res
kernel_size = 3
if act_res_flag == 'No':
act_res = None
else:
act_res = 'prelu'
head = [common.default_conv(args.n_colors, n_feats, kernel_size=9), nn.PReLU()]
body = [common.ResBlock(conv3x3, n_feats, kernel_size, bn=True, act=act_res) for _ in range(n_resblock)]
body.extend([conv3x3(n_feats, n_feats, kernel_size), nn.BatchNorm2d(n_feats)])
tail = [
common.Upsampler(conv3x3, scale, n_feats, act=act_res),
conv3x3(n_feats, args.n_colors, kernel_size)
]
self.head = nn.Sequential(*head)
self.body = nn.Sequential(*body)
self.tail = nn.Sequential(*tail)
if conv3x3 == common.default_conv:
# print('Loading from checkpoint {}'.format(args.pretrain_cluster))
# for (k1, v1), (k2, v2) in zip(self.state_dict().items(), torch.load(args.pretrain_cluster).items()):
# print('{:<50}\t{:<50}\t{} {}'.format(k1, k2, list(v1.shape), list(v2.shape)))
self.load_state_dict(torch.load(args.pretrain_cluster))
def __init__(self, opt):
super(NET, self).__init__()
n_resblocks = opt.n_resblocks
n_feats = opt.channels
bias = opt.bias
norm_type = opt.norm_type
act_type = opt.act_type
block_type = opt.block_type
head = [common.ConvBlock(4, n_feats, 5, act_type=act_type, bias=True)]
if block_type.lower() == 'rrdb':
resblock = [common.RRDB(n_feats, n_feats, 3,
1, bias, norm_type, act_type, 0.2)
for _ in range(n_resblocks)]
elif block_type.lower() == 'res':
resblock = [common.ResBlock(n_feats, 3, norm_type, act_type, res_scale=1, bias=bias)
for _ in range(n_resblocks)]
else:
raise RuntimeError('block_type is not supported')
resblock += [common.ConvBlock(n_feats, n_feats, 3, bias=True)]
tail = [common.ConvBlock(n_feats, 3, 3, bias=True)]
self.model = nn.Sequential(*head, common.ShortcutBlock(nn.Sequential(*resblock)), *tail)
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.xavier_normal_(m.weight)
m.weight.requires_grad = True
if m.bias is not None:
m.bias.data.zero_()
m.bias.requires_grad = True
def __init__(self, args, conv=common.default_conv):
super(EDSR, self).__init__()
n_resblock = args.n_resblocks
n_feats = args.n_feats
#kernel_size = 5 # My code 22.4.2018. origin is 3
kernel_size = 3
scale = args.scale[0]
act = nn.ReLU(True)
#act = common.Swish()
#rgb_mean = (0.4488, 0.4371, 0.4040)
#self.sub_mean = common.MeanShift(args.rgb_range, rgb_mean, -1)
# define head module
modules_head = [conv(args.n_colors, n_feats, kernel_size)]
# define body module
modules_body = [
common.ResBlock(
conv, n_feats, kernel_size, act=act, res_scale=args.res_scale) \
for _ in range(n_resblock)]
modules_body.append(conv(n_feats, n_feats, kernel_size))
# define tail module
modules_tail = [
common.Upsampler(conv, scale, n_feats, act=False),
conv(n_feats, args.n_colors, kernel_size)
]
#self.add_mean = common.MeanShift(args.rgb_range, rgb_mean, 1)
self.head = nn.Sequential(*modules_head)
self.body = nn.Sequential(*modules_body)
self.tail = nn.Sequential(*modules_tail)
def __init__(self,
in_channel,
kernel_size=3,
scale=2,
conv=common.default_conv):
super(MultisourceProjection, self).__init__()
deconv_ksize, stride, padding, up_factor = {
2: (6, 2, 2, 2),
3: (9, 3, 3, 3),
4: (6, 2, 2, 2)
}[scale]
self.up_attention = CrossScaleAttention(scale=up_factor)
self.down_attention = NonLocalAttention()
self.upsample = nn.Sequential(*[
nn.ConvTranspose2d(in_channel,
in_channel,
deconv_ksize,
stride=stride,
padding=padding),
nn.PReLU()
])
self.encoder = common.ResBlock(conv,
in_channel,
kernel_size,
act=nn.PReLU(),
res_scale=1)
def __init__(self, args, conv=common.default_conv):
super(KPRCN, self).__init__()
n_resgroups = args.n_resgroups
n_resblocks = args.n_resblocks
n_feats = args.nc_feats
kernel_size = args.kernel_size
reduction = args.reduction
self.nalu = args.nalu
act = nn.LeakyReLU(True)
# define head module
modules_head = [
conv(args.nc_input, args.nc_feats, args.kernel_size),
nn.LeakyReLU()
]
# define body module
modules_body = [
common.ResBlock(conv,
n_feats,
kernel_size,
act=act,
res_scale=args.res_scale)
for _ in range(n_resblocks - 2)
]
modules_body.append(conv(n_feats, n_feats, kernel_size))
self.nc_output = 3 if args.model == 'DPCN' else args.recon_kernel_size**2
# define tail module
modules_tail = [conv(args.nc_feats, self.nc_output, args.kernel_size)]
self.head = nn.Sequential(*modules_head)
self.body = nn.Sequential(*modules_body)
self.tail = nn.Sequential(*modules_tail)
def __init__(self, args, in_dim=6, conv=common.default_conv):
super(DNet, self).__init__()
n_resblocks = args.n_resblocks
n_feats = args.n_feats
kernel_size = 3
act = nn.ReLU(True)
#self.sub_mean = common.MeanShift(args.rgb_range)
#self.add_mean = common.MeanShift(args.rgb_range, sign=1)
# define head module
m_head = [conv(in_dim, n_feats, kernel_size)]
# define body module
m_body = [
common.ResBlock(conv,
n_feats,
kernel_size,
act=act,
res_scale=args.res_scale)
for _ in range(n_resblocks)
]
m_body.append(conv(n_feats, n_feats, kernel_size))
# define tail module
m_tail = [conv(n_feats, args.n_colors, kernel_size)]
self.head = nn.Sequential(*m_head)
self.body = nn.Sequential(*m_body)
self.tail = nn.Sequential(*m_tail)
def __init__(self, args, conv=common.default_conv):
super(PANET, self).__init__()
n_resblock = args.n_resblocks
n_feats = args.n_feats
kernel_size = 3
scale = args.scale[0]
rgb_mean = (0.4488, 0.4371, 0.4040)
rgb_std = (1.0, 1.0, 1.0)
self.sub_mean = common.MeanShift(args.rgb_range, rgb_mean, rgb_std)
msa = attention.PyramidAttention()
# define head module
m_head = [conv(args.n_colors, n_feats, kernel_size)]
# define body module
m_body = [
common.ResBlock(conv,
n_feats,
kernel_size,
nn.PReLU(),
res_scale=args.res_scale)
for _ in range(n_resblocks // 2)
]
m_body.append(msa)
for i in range(n_resblocks // 2):
m_body.append(
common.ResBlock(conv,
n_feats,
kernel_size,
nn.PReLU(),
res_scale=args.res_scale))
m_body.append(conv(n_feats, n_feats, kernel_size))
# define tail module
#m_tail = [
# common.Upsampler(conv, scale, n_feats, act=False),
# conv(n_feats, args.n_colors, kernel_size)
#]
m_tail = [conv(n_feats, args.n_colors, kernel_size)]
self.add_mean = common.MeanShift(args.rgb_range, rgb_mean, rgb_std, 1)
self.head = nn.Sequential(*m_head)
self.body = nn.Sequential(*m_body)
self.tail = nn.Sequential(*m_tail)
def __init__(self, scale_list, model_path='weight/MDSR_weight.pt'):
super(MDSR, self).__init__()
# args
self.scale_list = scale_list
input_channel = 3
output_channel = 3
num_block = 32
inp = 64
rgb_range = 255
res_scale = 0.1
act = nn.ReLU(True)
#act = nn.LeakyReLU(negative_slope=0.05, inplace=True)
# head
self.head = nn.Sequential(common.conv(3, inp, input_channel))
# pre_process
self.pre_process = nn.ModuleDict([
str(scale),
nn.Sequential(
common.ResBlock(inp, bias=True, act=act, res_scale=res_scale),
common.ResBlock(inp, bias=True, act=act, res_scale=res_scale))
] for scale in self.scale_list)
# body
self.body = nn.Sequential(*[
common.ResBlock(inp, bias=True, act=act, res_scale=res_scale)
for _ in range(num_block)
])
self.body.add_module(str(num_block), common.conv(inp, inp, 3))
#upsample
self.upsample = nn.ModuleDict(
[str(scale),
common.Upsampler(scale, inp, act=False, choice=0)]
for scale in self.scale_list)
# tail
self.tail = nn.Sequential(common.conv(inp, 3, output_channel))
self.sub_mean = common.MeanShift(rgb_range, sign=-1)
self.add_mean = common.MeanShift(rgb_range, sign=1)
self.model_path = model_path
self.load()
def __init__(self, in_channels, num=4, blocks=2):
super(CES, self).__init__()
print('num_RB:', num)
print('num_blocks', blocks)
self.blocks = blocks
if blocks == 2:
RBS = [
common.ResBlock(common.default_conv,
n_feats=in_channels,
kernel_size=3,
act=nn.PReLU(),
res_scale=1) for _ in range(num)
]
self.RBS = nn.Sequential(*RBS)
self.c1 = merge_block(
in_channels=in_channels,
out_channels=in_channels) #CE(in_channels=in_channels)
self.c2 = merge_block(
in_channels=in_channels,
out_channels=in_channels) #CE(in_channels=in_channels)
else:
RBS1 = [
common.ResBlock(common.default_conv,
n_feats=in_channels,
kernel_size=3,
act=nn.PReLU(),
res_scale=1) for _ in range(num // 2)
]
RBS2 = [
common.ResBlock(common.default_conv,
n_feats=in_channels,
kernel_size=3,
act=nn.PReLU(),
res_scale=1) for _ in range(num // 2)
]
self.RBS1 = nn.Sequential(*RBS1)
self.RBS2 = nn.Sequential(*RBS2)
self.c1 = merge_block(
in_channels=in_channels,
out_channels=in_channels) #CE(in_channels=in_channels)
self.c2 = merge_block(
in_channels=in_channels,
out_channels=in_channels) #CE(in_channels=in_channels)
self.c3 = merge_block(in_channels=in_channels,
out_channels=in_channels)
def __init__(self, args, conv=common.default_conv):
super(EDSR_switch, self).__init__()
n_resblocks = args.n_resblocks
n_feats = args.n_feats
kernel_size = 3
scale = args.scale[0]
act = nn.ReLU(True)
if args.n_colors == 3:
self.sub_mean = common.MeanShift(args.rgb_range)
self.add_mean = common.MeanShift(args.rgb_range, sign=1)
elif args.n_colors == 1:
self.sub_mean = common.MeanShift(args.rgb_range,
args.n_colors,
rgb_mean=(0.4300, ),
rgb_std=(1.0, ))
self.add_mean = common.MeanShift(args.rgb_range,
args.n_colors,
rgb_mean=(0.4300, ),
rgb_std=(1.0, ),
sign=1)
# define head module
m_head = [conv(args.n_colors, n_feats, kernel_size)]
# define body module
m_body = [
common.ResBlock(conv,
n_feats,
kernel_size,
act=act,
res_scale=args.res_scale)
for _ in range(n_resblocks)
]
m_body.append(conv(n_feats, n_feats, kernel_size))
# define tail module
m_tail = [
common.Upsampler(conv, scale, n_feats, act=False),
conv(n_feats, args.n_colors, kernel_size)
]
# define switch branch
m_branch = [
nn.Conv2d(1, 12, 3, padding=1),
nn.PReLU(num_parameters=12),
nn.Conv2d(12, 12, 3, padding=1),
nn.PReLU(num_parameters=12),
nn.Conv2d(12, 12, 3, padding=1),
nn.PReLU(num_parameters=12),
nn.ConvTranspose2d(12, 1, 9, stride=3, padding=3)
]
self.head = nn.Sequential(*m_head)
self.body = nn.Sequential(*m_body)
self.tail = nn.Sequential(*m_tail)
self.branch = nn.Sequential(*m_branch)
def __init__(self):
super(MWRN_lv2_head, self).__init__()
self.color_channel = 3
self.conv2_head_1 = common.BBlock(common.default_conv0,16*self.color_channel,256,3,bn=True)
self.conv2_head_2 = common.BBlock(common.default_conv0,512,256,3,bn=True)
self.res2_head = nn.Sequential(*[common.ResBlock(common.default_conv0,256,3) for i in range(4)])
self.conv2_head_3 = common.BBlock(common.default_conv0,1024,512,3,bn=True)
self.DWT = common.DWT()