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76d61a0 | 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 | from __future__ import annotations
import torch
from torch import nn
import torch.nn.functional as F
from ..openstl.models.simvp_model import Encoder, MidIncepNet, MidMetaNet, sampling_generator
from ..openstl.modules.simvp_modules import ConvSC
class BTAuxDecoder(nn.Module):
"""SimVP decoder with a CI readout and a shallow future-BT auxiliary head."""
def __init__(
self,
c_hid: int,
ci_out_channels: int,
bt_out_channels: int,
n_s: int,
spatio_kernel: int,
act_inplace: bool = True,
):
super().__init__()
samplings = sampling_generator(n_s, reverse=True)
self.dec = nn.Sequential(
*[
ConvSC(c_hid, c_hid, spatio_kernel, upsampling=s, act_inplace=act_inplace)
for s in samplings[:-1]
],
ConvSC(c_hid * 2, c_hid, spatio_kernel, upsampling=samplings[-1], act_inplace=act_inplace),
)
first_c = max(c_hid // 3 * 2, 1)
second_c = max(c_hid // 3, 1)
self.ci_readout = nn.Sequential(
nn.Conv2d(c_hid, first_c, 3, padding="same"),
nn.SiLU(),
nn.Conv2d(first_c, second_c, 3, padding="same"),
nn.SiLU(),
nn.Conv2d(second_c, ci_out_channels, 3, padding="same"),
)
bt_hidden = max(c_hid // 4, 1)
self.bt_head = nn.Sequential(
nn.Conv2d(c_hid, bt_hidden, 3, padding="same"),
nn.SiLU(),
nn.Conv2d(bt_hidden, bt_out_channels, 1),
)
def forward(self, hid: torch.Tensor, enc1: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:
for i in range(0, len(self.dec) - 1):
hid = self.dec[i](hid)
if hid.shape[-2:] != enc1.shape[-2:]:
hid = F.interpolate(hid, size=(enc1.shape[2], enc1.shape[3]), mode="bilinear", align_corners=False)
shared_feat = self.dec[-1](torch.cat([hid, enc1], dim=1))
return self.ci_readout(shared_feat), self.bt_head(shared_feat)
class SimVPBTAux(nn.Module):
"""SimVP CI model with a shallow future-BT auxiliary prediction branch.
Returns:
{
"ci": sigmoid CI map with shape (B, H, W),
"bt": normalized BT prediction with shape (B, T_bt, C_bt, H, W),
}
"""
def __init__(
self,
in_shape,
hid_S: int = 16,
hid_T: int = 256,
N_S: int = 4,
N_T: int = 4,
model_type: str = "gSTA",
mlp_ratio: float = 8.0,
drop: float = 0.0,
drop_path: float = 0.0,
spatio_kernel_enc: int = 3,
spatio_kernel_dec: int = 3,
act_inplace: bool = True,
bt_out_shape=(6, 1, None, None),
**kwargs,
):
super().__init__()
t_in, c_in, h, w = in_shape
self.out_shape = tuple(kwargs.get("out_shape", (1, 1, h, w)))
self.bt_out_shape = self._normalize_bt_out_shape(bt_out_shape, h, w)
self._validate_shapes()
kwargs["enc_C"] = hid_S
hid_h = int(h / 2 ** (N_S / 2))
hid_w = int(w / 2 ** (N_S / 2))
act_inplace = False
self.enc = Encoder(c_in, hid_S, N_S, spatio_kernel_enc, act_inplace=act_inplace)
self.dec = BTAuxDecoder(
c_hid=hid_S,
ci_out_channels=self.out_shape[1],
bt_out_channels=self.bt_out_shape[0] * self.bt_out_shape[1],
n_s=N_S,
spatio_kernel=spatio_kernel_dec,
act_inplace=act_inplace,
)
model_type = "gsta" if model_type is None else str(model_type).lower()
if model_type == "incepu":
self.hid = MidIncepNet(t_in * hid_S, hid_T, N_T)
else:
self.hid = MidMetaNet(
t_in * hid_S,
hid_T,
N_T,
input_resolution=(hid_h, hid_w),
model_type=model_type,
mlp_ratio=mlp_ratio,
drop=drop,
drop_path=drop_path,
**kwargs,
)
if t_in != self.out_shape[0]:
self.skipconv = nn.Sequential(
nn.Conv2d(t_in * hid_S, self.out_shape[0] * hid_S, 7, padding="same"),
nn.SiLU(),
)
@staticmethod
def _normalize_bt_out_shape(bt_out_shape, h: int, w: int) -> tuple[int, int, int, int]:
values = list(bt_out_shape)
if len(values) != 4:
raise ValueError(f"bt_out_shape must be [T, C, H, W], got {bt_out_shape}")
values[2] = h if values[2] is None else values[2]
values[3] = w if values[3] is None else values[3]
return tuple(int(v) for v in values)
def _validate_shapes(self) -> None:
if len(self.out_shape) != 4:
raise ValueError(f"out_shape must be [T, C, H, W], got {self.out_shape}")
if self.out_shape[0] != 1 or self.out_shape[1] != 1:
raise ValueError(f"SimVPBTAux expects CI out_shape [1, 1, H, W], got {self.out_shape}")
if self.bt_out_shape[2:] != self.out_shape[2:]:
raise ValueError(f"bt_out_shape H,W {self.bt_out_shape[2:]} must match out_shape H,W {self.out_shape[2:]}")
def forward(self, x_raw: torch.Tensor, **kwargs) -> dict[str, torch.Tensor]:
b, t, c, h, w = x_raw.shape
x = x_raw.view(b * t, c, h, w)
embed, skip = self.enc(x)
_, c_hid, h_hid, w_hid = embed.shape
z = embed.view(b, t, c_hid, h_hid, w_hid)
hid = self.hid(z)
hid = hid.reshape(b * self.out_shape[0], c_hid, h_hid, w_hid)
if hid.shape[0] != skip.shape[0]:
skip = skip.view(b, t, c_hid, h, w)
skip = skip.view(b, t * c_hid, h, w)
skip = self.skipconv(skip)
skip = skip.view(b * self.out_shape[0], c_hid, h, w)
ci_logits, bt_logits = self.dec(hid, skip)
ci = torch.sigmoid(ci_logits.reshape(b, self.out_shape[0], self.out_shape[1], h, w)).reshape(b, h, w)
bt = bt_logits.reshape(b, self.bt_out_shape[0], self.bt_out_shape[1], h, w)
return {"ci": ci, "bt": bt}
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