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3d02762 | 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 | #!/usr/bin/env python
"""Experiment 8-v2: stronger unified RGB+pose AfriSign encoder.
This file reuses the Exp8 data pipeline and training loop, but swaps in a
stronger shared encoder:
- temporal convolutional pose stem before the Transformer
- language/modality/task-conditioned residual adapters
- stronger projection head for supervised contrastive learning
It is intentionally a thin wrapper around `exp8_unified_mixed_encoder.py` so the
same manifests, loaders, metrics, and aggregation scripts remain compatible.
"""
from __future__ import annotations
import sys
from pathlib import Path
import torch
import torch.nn as nn
ROOT = Path(__file__).resolve().parents[1]
sys.path.insert(0, str(ROOT))
from experiments import exp8_unified_mixed_encoder as exp8 # noqa: E402
class TemporalPoseStem(nn.Module):
"""TransSLR-style temporal stem for landmark sequences."""
def __init__(self, feature_dim: int, hidden_dim: int, dropout: float) -> None:
super().__init__()
self.in_proj = nn.Linear(feature_dim, hidden_dim)
self.blocks = nn.ModuleList(
[
nn.Sequential(
nn.LayerNorm(hidden_dim),
nn.Conv1d(hidden_dim, hidden_dim, kernel_size=5, padding=2, groups=hidden_dim),
nn.GELU(),
nn.Conv1d(hidden_dim, hidden_dim, kernel_size=1),
nn.Dropout(dropout),
)
for _ in range(3)
]
)
self.out_norm = nn.LayerNorm(hidden_dim)
def forward(self, x: torch.Tensor) -> torch.Tensor:
x = self.in_proj(x)
for block in self.blocks:
y = block[0](x).transpose(1, 2)
y = block[1](y)
y = block[2](y)
y = block[3](y).transpose(1, 2)
y = block[4](y)
x = x + y
return self.out_norm(x)
class ConditionalAdapter(nn.Module):
"""Small residual adapter conditioned by language/modality/level/task context."""
def __init__(self, hidden_dim: int, bottleneck: int, dropout: float) -> None:
super().__init__()
self.norm = nn.LayerNorm(hidden_dim)
self.down = nn.Linear(hidden_dim, bottleneck)
self.up = nn.Linear(bottleneck, hidden_dim)
self.drop = nn.Dropout(dropout)
self.gate = nn.Sequential(nn.Linear(hidden_dim, hidden_dim), nn.Sigmoid())
def forward(self, x: torch.Tensor, context: torch.Tensor) -> torch.Tensor:
update = self.up(torch.nn.functional.gelu(self.down(self.norm(x))))
gate = self.gate(context).unsqueeze(1)
return x + self.drop(update * gate)
class StrongUnifiedAfriSignEncoder(nn.Module):
def __init__(
self,
*,
task_dims: dict[str, int],
num_languages: int,
hidden_dim: int,
feature_dim: int,
max_tokens: int,
layers: int,
heads: int,
ff_dim: int,
dropout: float,
rgb_backbone: str,
rgb_train_backbone: str,
rgb_pretrained: bool,
) -> None:
super().__init__()
self.task_keys = list(task_dims)
self.pose_stem = TemporalPoseStem(feature_dim, hidden_dim, dropout)
if rgb_backbone == "small_cnn":
self.rgb_cnn = exp8.SmallFrameCNN(hidden_dim)
elif rgb_backbone == "efficientnet_b0":
self.rgb_cnn = exp8.EfficientNetB0FrameEncoder(hidden_dim, rgb_train_backbone, rgb_pretrained)
else:
raise ValueError(f"Unknown rgb_backbone={rgb_backbone!r}")
self.cls = nn.Parameter(torch.zeros(1, 1, hidden_dim))
self.pos = nn.Embedding(max_tokens + 1, hidden_dim)
self.lang_emb = nn.Embedding(num_languages, hidden_dim)
self.modality_emb = nn.Embedding(len(exp8.MODALITY_IDS), hidden_dim)
self.level_emb = nn.Embedding(len(exp8.LEVEL_IDS), hidden_dim)
self.task_emb = nn.Embedding(len(self.task_keys), hidden_dim)
self.pre_adapter = ConditionalAdapter(hidden_dim, max(32, hidden_dim // 4), dropout)
enc_layer = nn.TransformerEncoderLayer(
d_model=hidden_dim,
nhead=heads,
dim_feedforward=ff_dim,
dropout=dropout,
batch_first=True,
norm_first=True,
)
self.encoder = nn.TransformerEncoder(enc_layer, layers, enable_nested_tensor=False)
self.post_adapter = ConditionalAdapter(hidden_dim, max(32, hidden_dim // 4), dropout)
self.norm = nn.LayerNorm(hidden_dim)
self.drop = nn.Dropout(dropout)
self.heads = nn.ModuleDict({key: nn.Linear(hidden_dim, dim) for key, dim in task_dims.items()})
self.projector = nn.Sequential(
nn.Linear(hidden_dim, hidden_dim),
nn.GELU(),
nn.LayerNorm(hidden_dim),
nn.Linear(hidden_dim, hidden_dim),
)
nn.init.trunc_normal_(self.cls, std=0.02)
def encode(self, batch: dict[str, torch.Tensor]) -> torch.Tensor:
if "pose" in batch:
x = self.pose_stem(batch["pose"])
elif "rgb" in batch:
rgb = batch["rgb"]
b, t, c, h, w = rgb.shape
x = self.rgb_cnn(rgb.reshape(b * t, c, h, w)).reshape(b, t, -1)
else:
raise ValueError("Batch must contain pose or rgb")
b, t, _ = x.shape
positions = torch.arange(t + 1, device=x.device)
context = (
self.lang_emb(batch["lang_idx"])
+ self.modality_emb(batch["modality_idx"])
+ self.level_emb(batch["level_idx"])
+ self.task_emb(batch["task_idx"])
)
x = torch.cat([self.cls.expand(b, -1, -1), x], dim=1)
x = x + self.pos(positions).unsqueeze(0) + context.unsqueeze(1)
x = self.pre_adapter(x, context)
x = self.encoder(x)
x = self.post_adapter(x, context)
return self.drop(self.norm(x[:, 0]))
def forward(self, batch: dict[str, torch.Tensor], task_key: str) -> torch.Tensor:
return self.heads[task_key](self.encode(batch))
def contrast_features(self, features: torch.Tensor) -> torch.Tensor:
return torch.nn.functional.normalize(self.projector(features), dim=1)
def main() -> None:
exp8.UnifiedAfriSignEncoder = StrongUnifiedAfriSignEncoder
exp8.main()
if __name__ == "__main__":
main()
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