File size: 8,894 Bytes
2b21abc | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 | import torch
from torch import nn
from modules import ConvSC, Inception
from utilpack import (ConvNeXtSubBlock, ConvMixerSubBlock, GASubBlock, gInception_ST,
HorNetSubBlock, MLPMixerSubBlock, MogaSubBlock, PoolFormerSubBlock,
SwinSubBlock, UniformerSubBlock, VANSubBlock, ViTSubBlock, TAUSubBlock)
def stride_generator(N, reverse=False):
strides = [1, 2]*10
if reverse: return list(reversed(strides[:N]))
else: return strides[:N]
class Encoder(nn.Module):
def __init__(self,C_in, C_hid, N_S):
super(Encoder,self).__init__()
strides = stride_generator(N_S)
self.enc = nn.Sequential(
ConvSC(C_in, C_hid, stride=strides[0]),
*[ConvSC(C_hid, C_hid, stride=s) for s in strides[1:]]
)
def forward(self,x):# B*4, 3, 128, 128
enc1 = self.enc[0](x)
latent = enc1
for i in range(1,len(self.enc)):
latent = self.enc[i](latent)
return latent,enc1
class Decoder(nn.Module):
def __init__(self,C_hid, C_out, N_S):
super(Decoder,self).__init__()
strides = stride_generator(N_S, reverse=True)
self.dec = nn.Sequential(
*[ConvSC(C_hid, C_hid, stride=s, transpose=True) for s in strides[:-1]],
ConvSC(2*C_hid, C_hid, stride=strides[-1], transpose=True)
)
self.readout = nn.Conv2d(C_hid, C_out, 1)
def forward(self, hid, enc1=None):
for i in range(0,len(self.dec)-1):
hid = self.dec[i](hid)
Y = self.dec[-1](torch.cat([hid, enc1], dim=1))
Y = self.readout(Y)
return Y
class Mid_Xnet(nn.Module):
def __init__(self, channel_in, channel_hid, N_T, incep_ker = [3,5,7,11], groups=8):
super(Mid_Xnet, self).__init__()
self.N_T = N_T
enc_layers = [Inception(channel_in, channel_hid//2, channel_hid, incep_ker= incep_ker, groups=groups)]
for i in range(1, N_T-1):
enc_layers.append(Inception(channel_hid, channel_hid//2, channel_hid, incep_ker= incep_ker, groups=groups))
enc_layers.append(Inception(channel_hid, channel_hid//2, channel_hid, incep_ker= incep_ker, groups=groups))
dec_layers = [Inception(channel_hid, channel_hid//2, channel_hid, incep_ker= incep_ker, groups=groups)]
for i in range(1, N_T-1):
dec_layers.append(Inception(2*channel_hid, channel_hid//2, channel_hid, incep_ker= incep_ker, groups=groups))
dec_layers.append(Inception(2*channel_hid, channel_hid//2, channel_in, incep_ker= incep_ker, groups=groups))
self.enc = nn.Sequential(*enc_layers)
self.dec = nn.Sequential(*dec_layers)
def forward(self, x):
B, T, C, H, W = x.shape
x = x.reshape(B, T*C, H, W)
# encoder
skips = []
z = x
for i in range(self.N_T):
z = self.enc[i](z)
if i < self.N_T - 1:
skips.append(z)
# decoder
z = self.dec[0](z)
for i in range(1, self.N_T):
z = self.dec[i](torch.cat([z, skips[-i]], dim=1))
y = z.reshape(B, T, C, H, W)
return y
class MetaBlock(nn.Module):
"""The hidden Translator of MetaFormer for SimVP"""
def __init__(self, in_channels, out_channels, input_resolution=None, model_type=None,
mlp_ratio=8., drop=0.0, drop_path=0.0, layer_i=0):
super(MetaBlock, self).__init__()
self.in_channels = in_channels
self.out_channels = out_channels
model_type = model_type.lower() if model_type is not None else 'gsta'
if model_type == 'gsta':
self.block = GASubBlock(
in_channels, kernel_size=21, mlp_ratio=mlp_ratio,
drop=drop, drop_path=drop_path, act_layer=nn.GELU)
elif model_type == 'convmixer':
self.block = ConvMixerSubBlock(in_channels, kernel_size=11, activation=nn.GELU)
elif model_type == 'convnext':
self.block = ConvNeXtSubBlock(
in_channels, mlp_ratio=mlp_ratio, drop=drop, drop_path=drop_path)
elif model_type == 'hornet':
self.block = HorNetSubBlock(in_channels, mlp_ratio=mlp_ratio, drop_path=drop_path)
elif model_type in ['mlp', 'mlpmixer']:
self.block = MLPMixerSubBlock(
in_channels, input_resolution, mlp_ratio=mlp_ratio, drop=drop, drop_path=drop_path)
elif model_type in ['moga', 'moganet']:
self.block = MogaSubBlock(
in_channels, mlp_ratio=mlp_ratio, drop_rate=drop, drop_path_rate=drop_path)
elif model_type == 'poolformer':
self.block = PoolFormerSubBlock(
in_channels, mlp_ratio=mlp_ratio, drop=drop, drop_path=drop_path)
elif model_type == 'swin':
self.block = SwinSubBlock(
in_channels, input_resolution, layer_i=layer_i, mlp_ratio=mlp_ratio,
drop=drop, drop_path=drop_path)
elif model_type == 'uniformer':
block_type = 'MHSA' if in_channels == out_channels and layer_i > 0 else 'Conv'
self.block = UniformerSubBlock(
in_channels, mlp_ratio=mlp_ratio, drop=drop,
drop_path=drop_path, block_type=block_type)
elif model_type == 'van':
self.block = VANSubBlock(
in_channels, mlp_ratio=mlp_ratio, drop=drop, drop_path=drop_path, act_layer=nn.GELU)
elif model_type == 'vit':
self.block = ViTSubBlock(
in_channels, mlp_ratio=mlp_ratio, drop=drop, drop_path=drop_path)
else:
assert False and "Invalid model_type in SimVP"
if in_channels != out_channels:
self.reduction = nn.Conv2d(
in_channels, out_channels, kernel_size=1, stride=1, padding=0)
def forward(self, x):
z = self.block(x)
return z if self.in_channels == self.out_channels else self.reduction(z)
class MidMetaNet(nn.Module):
"""The hidden Translator of MetaFormer for SimVP"""
def __init__(self, channel_in, channel_hid, N2,
input_resolution=None, model_type=None,
mlp_ratio=4., drop=0.0, drop_path=0.1):
super(MidMetaNet, self).__init__()
assert N2 >= 2 and mlp_ratio > 1
self.N2 = N2
dpr = [ # stochastic depth decay rule
x.item() for x in torch.linspace(1e-2, drop_path, self.N2)]
# downsample
enc_layers = [MetaBlock(
channel_in, channel_hid, input_resolution, model_type,
mlp_ratio, drop, drop_path=dpr[0], layer_i=0)]
# middle layers
for i in range(1, N2-1):
enc_layers.append(MetaBlock(
channel_hid, channel_hid, input_resolution, model_type,
mlp_ratio, drop, drop_path=dpr[i], layer_i=i))
# upsample
enc_layers.append(MetaBlock(
channel_hid, channel_in, input_resolution, model_type,
mlp_ratio, drop, drop_path=drop_path, layer_i=N2-1))
self.enc = nn.Sequential(*enc_layers)
def forward(self, x):
B, T, C, H, W = x.shape
x = x.reshape(B, T*C, H, W)
z = x
for i in range(self.N2):
z = self.enc[i](z)
y = z.reshape(B, T, C, H, W)
return y
class SimVP(nn.Module):
def __init__(self, hid_S=32, hid_T=256, N_S=2, N_T=8, incep_ker=[3,5,7,11], groups=4):
super(SimVP, self).__init__()
T, C, H, W = 36,1,72,72
self.enc = Encoder(C, hid_S, N_S)
self.hid = MidMetaNet(T * hid_S, hid_T, N_T,
input_resolution=(H, W), model_type="vit",
mlp_ratio=8, drop=0.0, drop_path=0.1)
self.dec = Decoder(hid_S, C, N_S)
def forward(self, x_raw):
B, T, C, H, W = x_raw.shape
x = x_raw.view(B*T, C, H, W)
embed, skip = self.enc(x)
_, C_, H_, W_ = embed.shape
z = embed.view(B, T, C_, H_, W_)
hid = self.hid(z)
hid = hid.reshape(B*T, C_, H_, W_)
Y = self.dec(hid, skip)
Y = Y.reshape(B, T, C, H, W)
return Y
class larres(nn.Module):
def __init__(self, hid_S=32, hid_T=256, N_S=2, N_T=8, incep_ker=[3,5,7,11], groups=4):
super(larres, self).__init__()
T, C, H, W = 36,1,72,72
self.enc = Encoder(C, hid_S, N_S)
self.hid = Mid_Xnet(T * hid_S, hid_T, N_T, incep_ker, groups)
self.dec = Decoder(hid_S, C, N_S)
def forward(self, x_raw):
B, T, C, H, W = x_raw.shape
x = x_raw.view(B*T, C, H, W)
embed, skip = self.enc(x)
_, C_, H_, W_ = embed.shape
z = embed.view(B, T, C_, H_, W_)
hid = self.hid(z)
hid = hid.reshape(B*T, C_, H_, W_)
Y = self.dec(hid, skip)
Y = Y.reshape(B, T, C, H, W)
return Y |