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b4b2877 | 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 | """Frame-level future forecasting models.
Three baselines (all sharing the same forecast head signature):
- TransformerForecast (our DAF-style)
- FUTRForecast (Transformer encoder + parallel query decoder)
- DeepConvLSTMForecast (Ordoñez & Roggen 2016 wearable HAR backbone)
All take a dict {mod: (B, T_obs, F_mod)} and output (B, T_fut, num_classes).
"""
from __future__ import annotations
from typing import Dict, List
import torch
import torch.nn as nn
import torch.nn.functional as F
# ---------------------------------------------------------------------------
# Shared per-modality projection: each modality -> hidden dim d_model
# ---------------------------------------------------------------------------
class _PerModalityProj(nn.Module):
def __init__(self, modality_dims: Dict[str, int], d_model: int):
super().__init__()
self.proj = nn.ModuleDict({
m: nn.Linear(d, d_model) for m, d in modality_dims.items()
})
self.mod_emb = nn.Parameter(torch.zeros(len(modality_dims), d_model))
nn.init.trunc_normal_(self.mod_emb, std=0.02)
self.mods = list(modality_dims.keys())
def forward(self, x: Dict[str, torch.Tensor]) -> torch.Tensor:
# Concatenate per-modality projections along time? Or sum?
# We sum modality-projected features per time step (with modality
# embedding broadcast). Equivalent to early-fusion at the d_model
# space and is what a "modality-aware Transformer" typically uses.
out = None
for i, m in enumerate(self.mods):
h = self.proj[m](x[m]) + self.mod_emb[i]
out = h if out is None else out + h
return out / len(self.mods) # (B, T_obs, d_model)
# ---------------------------------------------------------------------------
# 1. Transformer (DAF-style) forecast model
# ---------------------------------------------------------------------------
class TransformerForecast(nn.Module):
def __init__(self, modality_dims: Dict[str, int], num_classes: int,
t_obs: int, t_fut: int, d_model: int = 128,
n_heads: int = 4, n_layers: int = 2, dropout: float = 0.1):
super().__init__()
self.t_obs = t_obs
self.t_fut = t_fut
self.num_classes = num_classes
self.embed = _PerModalityProj(modality_dims, d_model)
self.pos = nn.Parameter(torch.zeros(1, t_obs, d_model))
nn.init.trunc_normal_(self.pos, std=0.02)
layer = nn.TransformerEncoderLayer(
d_model=d_model, nhead=n_heads, dim_feedforward=4 * d_model,
dropout=dropout, batch_first=True, activation="gelu",
)
self.encoder = nn.TransformerEncoder(layer, num_layers=n_layers)
self.queries = nn.Parameter(torch.zeros(1, t_fut, d_model))
nn.init.trunc_normal_(self.queries, std=0.02)
self.cross_attn = nn.MultiheadAttention(
d_model, n_heads, dropout=dropout, batch_first=True
)
self.norm = nn.LayerNorm(d_model)
self.head = nn.Linear(d_model, num_classes)
def forward(self, x: Dict[str, torch.Tensor]) -> torch.Tensor:
h = self.embed(x) + self.pos
h = self.encoder(h) # (B, T_obs, D)
q = self.queries.expand(h.size(0), -1, -1) # (B, T_fut, D)
out, _ = self.cross_attn(q, h, h, need_weights=False)
out = self.norm(out)
return self.head(out) # (B, T_fut, C)
# ---------------------------------------------------------------------------
# 2. FUTR-style forecast (Future Transformer, Gong et al. CVPR 2022)
# Same encoder + parallel query decoder. We add a small Transformer
# decoder so it's not literally identical to TransformerForecast.
# ---------------------------------------------------------------------------
class FUTRForecast(nn.Module):
def __init__(self, modality_dims: Dict[str, int], num_classes: int,
t_obs: int, t_fut: int, d_model: int = 128,
n_heads: int = 4, n_enc: int = 2, n_dec: int = 1,
dropout: float = 0.1):
super().__init__()
self.t_obs = t_obs
self.t_fut = t_fut
self.num_classes = num_classes
self.embed = _PerModalityProj(modality_dims, d_model)
self.pos = nn.Parameter(torch.zeros(1, t_obs, d_model))
nn.init.trunc_normal_(self.pos, std=0.02)
enc_layer = nn.TransformerEncoderLayer(
d_model=d_model, nhead=n_heads, dim_feedforward=4 * d_model,
dropout=dropout, batch_first=True, activation="gelu",
)
self.encoder = nn.TransformerEncoder(enc_layer, num_layers=n_enc)
dec_layer = nn.TransformerDecoderLayer(
d_model=d_model, nhead=n_heads, dim_feedforward=4 * d_model,
dropout=dropout, batch_first=True, activation="gelu",
)
self.decoder = nn.TransformerDecoder(dec_layer, num_layers=n_dec)
self.queries = nn.Parameter(torch.zeros(1, t_fut, d_model))
nn.init.trunc_normal_(self.queries, std=0.02)
self.head = nn.Linear(d_model, num_classes)
def forward(self, x: Dict[str, torch.Tensor]) -> torch.Tensor:
memory = self.encoder(self.embed(x) + self.pos) # (B, T_obs, D)
q = self.queries.expand(memory.size(0), -1, -1) # (B, T_fut, D)
out = self.decoder(q, memory)
return self.head(out) # (B, T_fut, C)
# ---------------------------------------------------------------------------
# 3. DeepConvLSTM-style forecast
# ---------------------------------------------------------------------------
class DeepConvLSTMForecast(nn.Module):
def __init__(self, modality_dims: Dict[str, int], num_classes: int,
t_obs: int, t_fut: int, conv_filters: int = 64,
lstm_hidden: int = 128, n_lstm_layers: int = 2,
dropout: float = 0.1):
super().__init__()
self.t_obs = t_obs
self.t_fut = t_fut
self.num_classes = num_classes
self.mods = list(modality_dims.keys())
in_ch = sum(modality_dims.values())
# Same 4-layer conv stack as the original DeepConvLSTM
layers = []
ch = in_ch
for i in range(4):
layers.append(nn.Sequential(
nn.Conv1d(ch, conv_filters, kernel_size=5, padding=2),
nn.BatchNorm1d(conv_filters),
nn.ReLU(),
nn.Dropout(dropout if i < 3 else 0.2),
))
ch = conv_filters
self.convs = nn.ModuleList(layers)
self.lstm = nn.LSTM(
conv_filters, lstm_hidden, num_layers=n_lstm_layers,
batch_first=True, dropout=dropout if n_lstm_layers > 1 else 0,
)
self.head = nn.Linear(lstm_hidden, t_fut * num_classes)
def forward(self, x: Dict[str, torch.Tensor]) -> torch.Tensor:
h = torch.cat([x[m] for m in self.mods], dim=-1) # (B, T_obs, F_total)
h = h.permute(0, 2, 1) # (B, F, T_obs)
for c in self.convs:
h = c(h)
h = h.permute(0, 2, 1) # (B, T_obs, conv_filters)
out, (h_n, _) = self.lstm(h)
feat = h_n[-1] # (B, lstm_hidden)
logits = self.head(feat).view(-1, self.t_fut, self.num_classes)
return logits
# ---------------------------------------------------------------------------
# 4. RU-LSTM (Furnari et al. RAL 2019, "Rolling-Unrolling LSTM for action
# anticipation"). Two-phase LSTM: a "rolling" phase encodes past, an
# "unrolling" phase autoregressively decodes future tokens.
# ---------------------------------------------------------------------------
class RULSTMForecast(nn.Module):
def __init__(self, modality_dims: Dict[str, int], num_classes: int,
t_obs: int, t_fut: int, d_model: int = 128,
n_lstm_layers: int = 2, dropout: float = 0.1):
super().__init__()
self.t_obs = t_obs
self.t_fut = t_fut
self.num_classes = num_classes
self.embed = _PerModalityProj(modality_dims, d_model)
self.rolling = nn.LSTM(
d_model, d_model, num_layers=n_lstm_layers,
batch_first=True, dropout=dropout if n_lstm_layers > 1 else 0,
)
self.unrolling = nn.LSTM(
d_model, d_model, num_layers=n_lstm_layers,
batch_first=True, dropout=dropout if n_lstm_layers > 1 else 0,
)
self.fut_init = nn.Parameter(torch.zeros(1, 1, d_model))
nn.init.trunc_normal_(self.fut_init, std=0.02)
self.head = nn.Linear(d_model, num_classes)
def forward(self, x: Dict[str, torch.Tensor]) -> torch.Tensor:
h_past = self.embed(x) # (B, T_obs, D)
_, (h_n, c_n) = self.rolling(h_past)
B = h_past.size(0)
# Use a learned initial future token, repeated T_fut times
fut_input = self.fut_init.expand(B, self.t_fut, -1)
out, _ = self.unrolling(fut_input, (h_n, c_n))
return self.head(out) # (B, T_fut, C)
# ---------------------------------------------------------------------------
# 5. AVT (Girdhar & Grauman ICCV 2021, "Anticipative Video Transformer").
# Causal Transformer over the concatenation of past + future tokens.
# ---------------------------------------------------------------------------
class AVTForecast(nn.Module):
def __init__(self, modality_dims: Dict[str, int], num_classes: int,
t_obs: int, t_fut: int, d_model: int = 128,
n_heads: int = 4, n_layers: int = 2, dropout: float = 0.1):
super().__init__()
self.t_obs = t_obs
self.t_fut = t_fut
self.num_classes = num_classes
self.embed = _PerModalityProj(modality_dims, d_model)
seq_len = t_obs + t_fut
self.pos = nn.Parameter(torch.zeros(1, seq_len, d_model))
nn.init.trunc_normal_(self.pos, std=0.02)
layer = nn.TransformerEncoderLayer(
d_model=d_model, nhead=n_heads, dim_feedforward=4 * d_model,
dropout=dropout, batch_first=True, activation="gelu",
)
self.encoder = nn.TransformerEncoder(layer, num_layers=n_layers)
self.fut_tokens = nn.Parameter(torch.zeros(1, t_fut, d_model))
nn.init.trunc_normal_(self.fut_tokens, std=0.02)
self.head = nn.Linear(d_model, num_classes)
# Causal mask over concatenated [past | future] sequence
mask = torch.triu(torch.ones(seq_len, seq_len), diagonal=1).bool()
self.register_buffer("causal_mask", mask)
def forward(self, x: Dict[str, torch.Tensor]) -> torch.Tensor:
h_past = self.embed(x) # (B, T_obs, D)
B = h_past.size(0)
h_fut = self.fut_tokens.expand(B, -1, -1) # (B, T_fut, D)
seq = torch.cat([h_past, h_fut], dim=1) + self.pos
out = self.encoder(seq, mask=self.causal_mask)
out_fut = out[:, self.t_obs:, :]
return self.head(out_fut) # (B, T_fut, C)
# ---------------------------------------------------------------------------
# Builder
# ---------------------------------------------------------------------------
def build_forecast_model(name: str, modality_dims: Dict[str, int],
num_classes: int, t_obs: int, t_fut: int,
d_model: int = 128, dropout: float = 0.1) -> nn.Module:
name = name.lower()
if name in ("daf", "transformer"):
return TransformerForecast(modality_dims, num_classes,
t_obs=t_obs, t_fut=t_fut,
d_model=d_model, dropout=dropout)
if name == "futr":
return FUTRForecast(modality_dims, num_classes,
t_obs=t_obs, t_fut=t_fut,
d_model=d_model, dropout=dropout)
if name == "deepconvlstm":
return DeepConvLSTMForecast(modality_dims, num_classes,
t_obs=t_obs, t_fut=t_fut,
dropout=dropout)
if name in ("rulstm", "ru-lstm", "ru_lstm"):
return RULSTMForecast(modality_dims, num_classes,
t_obs=t_obs, t_fut=t_fut,
d_model=d_model, dropout=dropout)
if name == "avt":
return AVTForecast(modality_dims, num_classes,
t_obs=t_obs, t_fut=t_fut,
d_model=d_model, dropout=dropout)
raise ValueError(f"Unknown forecast model: {name!r}")
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