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dbc6675 | 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 | import torch
import torch.nn as nn
class StateCentricLSTM_Delta(nn.Module):
def __init__(
self, input_dim, hidden_dim=256, num_layers=2, embed_dim=32, use_projection=True
):
super().__init__()
self.use_projection = use_projection
if self.use_projection:
# Projection: High-Dim Sparse -> Low-Dim Dense
self.encoder = nn.Linear(input_dim, embed_dim)
lstm_input_size = embed_dim * 2
else:
# No Projection: Use raw input dimensions
lstm_input_size = input_dim * 2
# LSTM
self.lstm = nn.LSTM(
input_size=lstm_input_size,
hidden_size=hidden_dim,
num_layers=num_layers,
batch_first=True,
)
# Head: Projects back to Input Dim (Delta)
# No LayerNorm, No ReLU at the end
# We need to predict negative values (subtractions)
self.head = nn.Linear(hidden_dim, input_dim)
def forward(self, state_seq, goal_seq, lengths=None, hidden=None):
"""
Returns:
pred_delta: [B, T, Input_D] (The predicted CHANGE)
hidden: LSTM hidden state
"""
# 1. Encode (or pass through)
if self.use_projection:
state_emb = torch.relu(self.encoder(state_seq))
goal_emb = torch.relu(self.encoder(goal_seq))
else:
state_emb = state_seq
goal_emb = goal_seq
_, T, _ = state_emb.shape
# 2. Expand Goal
goal_expanded = goal_emb.unsqueeze(1).expand(-1, T, -1)
# 3. Concat
lstm_input = torch.cat([state_emb, goal_expanded], dim=2)
# 4. Pack
if lengths is not None:
lstm_input = torch.nn.utils.rnn.pack_padded_sequence(
lstm_input, lengths.cpu(), batch_first=True, enforce_sorted=False
)
# 5. LSTM
out, hidden = self.lstm(lstm_input, hidden)
# 6. Unpack
if lengths is not None:
out, _ = torch.nn.utils.rnn.pad_packed_sequence(out, batch_first=True)
# 7. Predict Delta
pred_delta = self.head(out)
return pred_delta, hidden
class StateCentricLSTM(nn.Module):
def __init__(
self, input_dim, hidden_dim=256, num_layers=2, embed_dim=32, use_projection=True
):
super().__init__()
self.use_projection = use_projection
if self.use_projection:
# 1. Add a Projection Layer (Dimensionality Reduction)
self.encoder = nn.Linear(input_dim, embed_dim)
lstm_input_size = embed_dim * 2
else:
lstm_input_size = input_dim * 2
# LSTM
self.lstm = nn.LSTM(
input_size=lstm_input_size,
hidden_size=hidden_dim,
num_layers=num_layers,
batch_first=True,
)
# Head projects back to FULL input_dim
self.head = nn.Sequential(
nn.Linear(hidden_dim, hidden_dim),
nn.LayerNorm(hidden_dim),
nn.ReLU(),
nn.Linear(hidden_dim, input_dim),
)
def forward(self, state_seq, goal_seq, lengths=None, hidden=None):
"""
state_seq: [B, T, Input_D]
goal_seq: [B, D] (will be expanded to [B, T, D])
"""
# 1. Project High-Dim Sparse -> Low-Dim Dense (Optional)
if self.use_projection:
state_emb = torch.relu(self.encoder(state_seq)) # [B, T, embed_dim]
goal_emb = torch.relu(self.encoder(goal_seq)) # [B, embed_dim]
else:
state_emb = state_seq
goal_emb = goal_seq
_, T, _ = state_emb.shape
# Expand goal to match sequence length
goal_expanded = goal_emb.unsqueeze(1).expand(-1, T, -1)
# Concatenate: [B, T, 2D]
lstm_input = torch.cat([state_emb, goal_expanded], dim=2)
# Pack sequences if lengths are provided
if lengths is not None:
lstm_input = torch.nn.utils.rnn.pack_padded_sequence(
lstm_input, lengths.cpu(), batch_first=True, enforce_sorted=False
)
# LSTM Forward
out, hidden = self.lstm(lstm_input, hidden)
if lengths is not None:
out, _ = torch.nn.utils.rnn.pad_packed_sequence(out, batch_first=True)
# Project to next state
pred_next_state = self.head(out)
return pred_next_state, hidden
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