File size: 18,472 Bytes
ff4becd | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 | import torch
from torch import nn
import torch.nn.functional as F
import math
from ..layers.RevIN import RevIN
from .ModernTCN_Layer import series_decomp, Flatten_Head
class LayerNorm(nn.Module):
def __init__(self, channels, eps=1e-6, data_format="channels_last"):
super(LayerNorm, self).__init__()
self.norm = nn.Layernorm(channels)
def forward(self, x):
B, M, D, N = x.shape
x = x.permute(0, 1, 3, 2)
x = x.reshape(B * M, N, D)
x = self.norm(x)
x = x.reshape(B, M, N, D)
x = x.permute(0, 1, 3, 2)
return x
def get_conv1d(in_channels, out_channels, kernel_size, stride, padding, dilation, groups, bias):
return nn.Conv1d(in_channels=in_channels, out_channels=out_channels, kernel_size=kernel_size, stride=stride,
padding=padding, dilation=dilation, groups=groups, bias=bias)
def get_bn(channels):
return nn.BatchNorm1d(channels)
def conv_bn(in_channels, out_channels, kernel_size, stride, padding, groups, dilation=1,bias=False):
if padding is None:
padding = kernel_size // 2
result = nn.Sequential()
result.add_module('conv', get_conv1d(in_channels=in_channels, out_channels=out_channels, kernel_size=kernel_size,
stride=stride, padding=padding, dilation=dilation, groups=groups, bias=bias))
result.add_module('bn', get_bn(out_channels))
return result
def fuse_bn(conv, bn):
kernel = conv.weight
running_mean = bn.running_mean
running_var = bn.running_var
gamma = bn.weight
beta = bn.bias
eps = bn.eps
std = (running_var + eps).sqrt()
t = (gamma / std).reshape(-1, 1, 1)
return kernel * t, beta - running_mean * gamma / std
class ReparamLargeKernelConv(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size,
stride, groups,
small_kernel,
small_kernel_merged=False, nvars=7):
super(ReparamLargeKernelConv, self).__init__()
self.kernel_size = kernel_size
self.small_kernel = small_kernel
# We assume the conv does not change the feature map size, so padding = k//2. Otherwise, you may configure padding as you wish, and change the padding of small_conv accordingly.
padding = kernel_size // 2
if small_kernel_merged:
self.lkb_reparam = nn.Conv1d(in_channels=in_channels, out_channels=out_channels, kernel_size=kernel_size,
stride=stride, padding=padding, dilation=1, groups=groups, bias=True)
else:
self.lkb_origin = conv_bn(in_channels=in_channels, out_channels=out_channels, kernel_size=kernel_size,
stride=stride, padding=padding, dilation=1, groups=groups,bias=False)
if small_kernel is not None:
assert small_kernel <= kernel_size, 'The kernel size for re-param cannot be larger than the large kernel!'
self.small_conv = conv_bn(in_channels=in_channels, out_channels=out_channels,
kernel_size=small_kernel,
stride=stride, padding=small_kernel // 2, groups=groups, dilation=1,bias=False)
def forward(self, inputs):
if hasattr(self, 'lkb_reparam'):
out = self.lkb_reparam(inputs)
else:
out = self.lkb_origin(inputs)
if hasattr(self, 'small_conv'):
out += self.small_conv(inputs)
return out
def PaddingTwoEdge1d(self,x,pad_length_left,pad_length_right,pad_values=0):
D_out,D_in,ks=x.shape
if pad_values ==0:
pad_left = torch.zeros(D_out,D_in,pad_length_left)
pad_right = torch.zeros(D_out,D_in,pad_length_right)
else:
pad_left = torch.ones(D_out, D_in, pad_length_left) * pad_values
pad_right = torch.ones(D_out, D_in, pad_length_right) * pad_values
x = torch.cat([pad_left,x],dims=-1)
x = torch.cat([x,pad_right],dims=-1)
return x
def get_equivalent_kernel_bias(self):
eq_k, eq_b = fuse_bn(self.lkb_origin.conv, self.lkb_origin.bn)
if hasattr(self, 'small_conv'):
small_k, small_b = fuse_bn(self.small_conv.conv, self.small_conv.bn)
eq_b += small_b
eq_k += self.PaddingTwoEdge1d(small_k, (self.kernel_size - self.small_kernel) // 2,
(self.kernel_size - self.small_kernel) // 2, 0)
return eq_k, eq_b
def merge_kernel(self):
eq_k, eq_b = self.get_equivalent_kernel_bias()
self.lkb_reparam = nn.Conv1d(in_channels=self.lkb_origin.conv.in_channels,
out_channels=self.lkb_origin.conv.out_channels,
kernel_size=self.lkb_origin.conv.kernel_size, stride=self.lkb_origin.conv.stride,
padding=self.lkb_origin.conv.padding, dilation=self.lkb_origin.conv.dilation,
groups=self.lkb_origin.conv.groups, bias=True)
self.lkb_reparam.weight.data = eq_k
self.lkb_reparam.bias.data = eq_b
self.__delattr__('lkb_origin')
if hasattr(self, 'small_conv'):
self.__delattr__('small_conv')
class Block(nn.Module):
def __init__(self, large_size, small_size, dmodel, dff, nvars, small_kernel_merged=False, drop=0.1):
super(Block, self).__init__()
self.dw = ReparamLargeKernelConv(in_channels=nvars * dmodel, out_channels=nvars * dmodel,
kernel_size=large_size, stride=1, groups=nvars * dmodel,
small_kernel=small_size, small_kernel_merged=small_kernel_merged, nvars=nvars)
self.norm = nn.BatchNorm1d(dmodel)
#convffn1
self.ffn1pw1 = nn.Conv1d(in_channels=nvars * dmodel, out_channels=nvars * dff, kernel_size=1, stride=1,
padding=0, dilation=1, groups=nvars)
self.ffn1act = nn.GELU()
self.ffn1pw2 = nn.Conv1d(in_channels=nvars * dff, out_channels=nvars * dmodel, kernel_size=1, stride=1,
padding=0, dilation=1, groups=nvars)
self.ffn1drop1 = nn.Dropout(drop)
self.ffn1drop2 = nn.Dropout(drop)
#convffn2
self.ffn2pw1 = nn.Conv1d(in_channels=nvars * dmodel, out_channels=nvars * dff, kernel_size=1, stride=1,
padding=0, dilation=1, groups=dmodel)
self.ffn2act = nn.GELU()
self.ffn2pw2 = nn.Conv1d(in_channels=nvars * dff, out_channels=nvars * dmodel, kernel_size=1, stride=1,
padding=0, dilation=1, groups=dmodel)
self.ffn2drop1 = nn.Dropout(drop)
self.ffn2drop2 = nn.Dropout(drop)
self.ffn_ratio = dff//dmodel
def forward(self,x):
input = x
B, M, D, N = x.shape
x = x.reshape(B,M*D,N)
x = self.dw(x)
x = x.reshape(B,M,D,N)
x = x.reshape(B*M,D,N)
x = self.norm(x)
x = x.reshape(B, M, D, N)
x = x.reshape(B, M * D, N)
x = self.ffn1drop1(self.ffn1pw1(x))
x = self.ffn1act(x)
x = self.ffn1drop2(self.ffn1pw2(x))
x = x.reshape(B, M, D, N)
x = x.permute(0, 2, 1, 3)
x = x.reshape(B, D * M, N)
x = self.ffn2drop1(self.ffn2pw1(x))
x = self.ffn2act(x)
x = self.ffn2drop2(self.ffn2pw2(x))
x = x.reshape(B, D, M, N)
x = x.permute(0, 2, 1, 3)
x = input + x
return x
class Stage(nn.Module):
def __init__(self, ffn_ratio, num_blocks, large_size, small_size, dmodel, dw_model, nvars,
small_kernel_merged=False, drop=0.1):
super(Stage, self).__init__()
d_ffn = dmodel * ffn_ratio
blks = []
for i in range(num_blocks):
blk = Block(large_size=large_size, small_size=small_size, dmodel=dmodel, dff=d_ffn, nvars=nvars, small_kernel_merged=small_kernel_merged, drop=drop)
blks.append(blk)
self.blocks = nn.ModuleList(blks)
def forward(self, x):
for blk in self.blocks:
x = blk(x)
return x
class ModernTCN(nn.Module):
def __init__(self,patch_size,patch_stride, stem_ratio, downsample_ratio, ffn_ratio, num_blocks, large_size, small_size, dims, dw_dims,
nvars, small_kernel_merged=False, backbone_dropout=0.1, head_dropout=0.1, use_multi_scale=True, revin=True, affine=True,
subtract_last=False, freq=None, seq_len=512, c_in=7, individual=False, target_window=96):
super(ModernTCN, self).__init__()
# RevIN
self.revin = revin
if self.revin:
self.revin_layer = RevIN(c_in, affine=affine, subtract_last=subtract_last)
# stem layer & down sampling layers(if needed)
self.downsample_layers = nn.ModuleList()
stem = nn.Sequential(
nn.Conv1d(1, dims[0], kernel_size=patch_size, stride=patch_stride),
nn.BatchNorm1d(dims[0])
)
self.downsample_layers.append(stem)
for i in range(3):
downsample_layer = nn.Sequential(
nn.BatchNorm1d(dims[i]),
nn.Conv1d(dims[i], dims[i + 1], kernel_size=downsample_ratio, stride=downsample_ratio),
)
self.downsample_layers.append(downsample_layer)
self.patch_size = patch_size
self.patch_stride = patch_stride
self.downsample_ratio = downsample_ratio
if freq == 'h':
time_feature_num = 4
elif freq == 't':
time_feature_num = 5
else:
raise NotImplementedError("time_feature_num should be 4 or 5")
self.te_patch = nn.Sequential(
nn.Conv1d(time_feature_num, time_feature_num, kernel_size=patch_size, stride=patch_stride,groups=time_feature_num),
nn.Conv1d(time_feature_num, dims[0], kernel_size=1, stride=1, groups=1),
nn.BatchNorm1d(dims[0]))
# backbone
self.num_stage = len(num_blocks)
self.stages = nn.ModuleList()
for stage_idx in range(self.num_stage):
layer = Stage(ffn_ratio, num_blocks[stage_idx], large_size[stage_idx], small_size[stage_idx], dmodel=dims[stage_idx],
dw_model=dw_dims[stage_idx], nvars=nvars, small_kernel_merged=small_kernel_merged, drop=backbone_dropout)
self.stages.append(layer)
# Multi scale fusing (if needed)
self.use_multi_scale = use_multi_scale
self.up_sample_ratio = downsample_ratio
self.lat_layer = nn.ModuleList()
self.smooth_layer = nn.ModuleList()
self.up_sample_conv = nn.ModuleList()
for i in range(self.num_stage):
align_dim = dims[-1]
lat = nn.Conv1d(dims[i], align_dim, kernel_size=1,
stride=1)
self.lat_layer.append(lat)
smooth = nn.Conv1d(align_dim, align_dim, kernel_size=3, stride=1, padding=1)
self.smooth_layer.append(smooth)
up_conv = nn.Sequential(
nn.ConvTranspose1d(align_dim, align_dim, kernel_size=self.up_sample_ratio, stride=self.up_sample_ratio),
nn.BatchNorm1d(align_dim))
self.up_sample_conv.append(up_conv)
# head
patch_num = seq_len // patch_stride
self.n_vars = c_in
self.individual = individual
d_model = dims[-1]
if use_multi_scale:
self.head_nf = d_model * patch_num
self.head = Flatten_Head(self.individual, self.n_vars, self.head_nf, target_window,
head_dropout=head_dropout)
else:
if patch_num % pow(downsample_ratio,(self.num_stage - 1)) == 0:
self.head_nf = d_model * patch_num // pow(downsample_ratio,(self.num_stage - 1))
else:
self.head_nf = d_model * (patch_num // pow(downsample_ratio, (self.num_stage - 1))+1)
self.head = Flatten_Head(self.individual, self.n_vars, self.head_nf, target_window,
head_dropout=head_dropout)
def up_sample(self, x, upsample_ratio):
_, _, _, N = x.shape
return F.upsample(x, size=N, scale_factor=upsample_ratio, mode='bilinear')
def forward_feature(self, x, te=None):
B,M,L=x.shape
x = x.unsqueeze(-2)
for i in range(self.num_stage):
B, M, D, N = x.shape
x = x.reshape(B * M, D, N)
if i==0:
if self.patch_size != self.patch_stride:
# stem layer padding
pad_len = self.patch_size - self.patch_stride
pad = x[:,:,-1:].repeat(1,1,pad_len)
x = torch.cat([x,pad],dim=-1)
else:
if N % self.downsample_ratio != 0:
pad_len = self.downsample_ratio - (N % self.downsample_ratio)
x = torch.cat([x, x[:, :, -pad_len:]],dim=-1)
x = self.downsample_layers[i](x)
_, D_, N_ = x.shape
x = x.reshape(B, M, D_, N_)
x = self.stages[i](x)
return x
def forward(self, x, te=None):
# instance norm
if self.revin:
x = x.permute(0, 2, 1)
x = self.revin_layer(x, 'norm')
x = x.permute(0, 2, 1)
x = self.forward_feature(x,te)
x = self.head(x)
# de-instance norm
if self.revin:
x = x.permute(0, 2, 1)
x = self.revin_layer(x, 'denorm')
x = x.permute(0, 2, 1)
return x
def structural_reparam(self):
for m in self.modules():
if hasattr(m, 'merge_kernel'):
m.merge_kernel()
class Model(nn.Module):
def __init__(self, configs):
super(Model, self).__init__()
# hyper param
self.stem_ratio = configs.stem_ratio
self.downsample_ratio = configs.downsample_ratio
self.ffn_ratio = configs.ffn_ratio
self.num_blocks = configs.num_blocks
self.large_size = configs.large_size
self.small_size = configs.small_size
self.dims = configs.dims
self.dw_dims = configs.dw_dims
self.nvars = configs.enc_in
self.small_kernel_merged = configs.small_kernel_merged
self.drop_backbone = configs.dropout
self.drop_head = configs.head_dropout
self.use_multi_scale = configs.use_multi_scale
self.revin = configs.revin
self.affine = configs.affine
self.subtract_last = configs.subtract_last
self.freq = configs.freq
self.seq_len = configs.seq_len
self.c_in = self.nvars,
self.individual = configs.individual
self.target_window = configs.pred_len
self.kernel_size = configs.kernel_size
self.patch_size = configs.patch_size
self.patch_stride = configs.patch_stride
# decomp
self.decomposition = configs.decomposition
if self.decomposition:
self.decomp_module = series_decomp(self.kernel_size)
self.model_res = ModernTCN(patch_size=self.patch_size,patch_stride=self.patch_stride,stem_ratio=self.stem_ratio, downsample_ratio=self.downsample_ratio, ffn_ratio=self.ffn_ratio, num_blocks=self.num_blocks, large_size=self.large_size, small_size=self.small_size, dims=self.dims, dw_dims=self.dw_dims,
nvars=self.nvars, small_kernel_merged=self.small_kernel_merged, backbone_dropout=self.drop_backbone, head_dropout=self.drop_head, use_multi_scale=self.use_multi_scale, revin=self.revin, affine=self.affine,
subtract_last=self.subtract_last, freq=self.freq, seq_len=self.seq_len, c_in=self.c_in, individual=self.individual, target_window=self.target_window)
self.model_trend = ModernTCN(patch_size=self.patch_size,patch_stride=self.patch_stride,stem_ratio=self.stem_ratio, downsample_ratio=self.downsample_ratio, ffn_ratio=self.ffn_ratio, num_blocks=self.num_blocks, large_size=self.large_size, small_size=self.small_size, dims=self.dims, dw_dims=self.dw_dims,
nvars=self.nvars, small_kernel_merged=self.small_kernel_merged, backbone_dropout=self.drop_backbone, head_dropout=self.drop_head, use_multi_scale=self.use_multi_scale, revin=self.revin, affine=self.affine,
subtract_last=self.subtract_last, freq=self.freq, seq_len=self.seq_len, c_in=self.c_in, individual=self.individual, target_window=self.target_window)
else:
self.model = ModernTCN(patch_size=self.patch_size,patch_stride=self.patch_stride,stem_ratio=self.stem_ratio, downsample_ratio=self.downsample_ratio, ffn_ratio=self.ffn_ratio, num_blocks=self.num_blocks, large_size=self.large_size, small_size=self.small_size, dims=self.dims, dw_dims=self.dw_dims,
nvars=self.nvars, small_kernel_merged=self.small_kernel_merged, backbone_dropout=self.drop_backbone, head_dropout=self.drop_head, use_multi_scale=self.use_multi_scale, revin=self.revin, affine=self.affine,
subtract_last=self.subtract_last, freq=self.freq, seq_len=self.seq_len, c_in=self.c_in, individual=self.individual, target_window=self.target_window)
def forward(self, x, te=None):
if self.decomposition:
res_init, trend_init = self.decomp_module(x)
res_init, trend_init = res_init.permute(0, 2, 1), trend_init.permute(0, 2, 1)
if te is not None:
te = te.permute(0, 2, 1)
res = self.model_res(res_init, te)
trend = self.model_trend(trend_init, te)
x = res + trend
x = x.permute(0, 2, 1)
else:
x = x.permute(0, 2, 1)
if te is not None:
te = te.permute(0, 2, 1)
x = self.model(x, te)
x = x.permute(0, 2, 1)
return x
|