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6494534 | 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 | from __future__ import annotations
try:
import torch
from torch import nn
except ImportError: # pragma: no cover
torch = None
nn = None
class ChartEncoder(nn.Module if nn is not None else object):
"""Encode a chart summary into a compact CTT chart token.
The first implementation uses exported numeric chart summaries such as the
base action chunk. When visual-language embeddings are exported, the same
module can consume them without changing the CTT operator interface.
"""
def __init__(self, input_dim: int, hidden_dim: int = 128, output_dim: int = 64) -> None:
if nn is None: # pragma: no cover
raise ImportError("ChartEncoder requires torch")
super().__init__()
self.net = nn.Sequential(
nn.Linear(input_dim, hidden_dim),
nn.ReLU(),
nn.Linear(hidden_dim, output_dim),
nn.LayerNorm(output_dim),
)
def forward(self, chart_features: "torch.Tensor") -> "torch.Tensor":
return self.net(chart_features)
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