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Published baseline models for DailyAct-5M benchmark.
ASFormer: Transformer for Action Segmentation (Yi et al., BMVC 2021)
- Multi-stage encoder-decoder transformer with dilated attention
- For temporal action segmentation (Exp 2) and contact detection (Exp 3)
TinyHAR: Lightweight Deep Learning Model for HAR (Zhou et al., ISWC 2022 Best Paper)
- Multi-scale temporal convolution + cross-channel attention + temporal pooling
- Implemented as backbone in models.py for scene recognition (Exp 1)
"""
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
# ============================================================
# Positional Encoding (shared)
# ============================================================
class PositionalEncoding1D(nn.Module):
"""Sinusoidal positional encoding."""
def __init__(self, d_model, dropout=0.1, max_len=10000):
super().__init__()
self.dropout = nn.Dropout(p=dropout)
pe = torch.zeros(max_len, d_model)
position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(1)
div_term = torch.exp(
torch.arange(0, d_model, 2).float() * (-math.log(10000.0) / d_model)
)
pe[:, 0::2] = torch.sin(position * div_term)
if d_model % 2 == 1:
pe[:, 1::2] = torch.cos(position * div_term[:-1])
else:
pe[:, 1::2] = torch.cos(position * div_term)
pe = pe.unsqueeze(0) # (1, max_len, d_model)
self.register_buffer('pe', pe)
def forward(self, x):
x = x + self.pe[:, :x.size(1)]
return self.dropout(x)
# ============================================================
# ASFormer (Yi et al., BMVC 2021)
# ============================================================
class ConvFeedForward(nn.Module):
"""Position-wise convolution feed-forward used in ASFormer."""
def __init__(self, d_model, kernel_size=3, dropout=0.1):
super().__init__()
self.norm = nn.LayerNorm(d_model)
self.conv1 = nn.Conv1d(d_model, d_model * 2, kernel_size, padding=kernel_size // 2)
self.conv2 = nn.Conv1d(d_model * 2, d_model, 1)
self.dropout = nn.Dropout(dropout)
def forward(self, x):
# x: (B, T, D)
residual = x
x = self.norm(x)
x = x.permute(0, 2, 1) # (B, D, T)
x = self.dropout(F.relu(self.conv1(x)))
x = self.dropout(self.conv2(x))
x = x.permute(0, 2, 1) # (B, T, D)
return residual + x
class DilatedAttention(nn.Module):
"""Multi-head self-attention with dilated temporal mask.
At dilation d and window w, position t attends to positions
{t + k*d : k in [-w, w]}, creating a hierarchical receptive field.
"""
def __init__(self, d_model, dilation, num_heads=1, dropout=0.1, window_size=5):
super().__init__()
self.d_model = d_model
self.dilation = dilation
self.window_size = window_size
self.num_heads = num_heads
self.head_dim = d_model // num_heads
self.norm = nn.LayerNorm(d_model)
self.qkv = nn.Linear(d_model, 3 * d_model)
self.out_proj = nn.Linear(d_model, d_model)
self.dropout = nn.Dropout(dropout)
# Cache for dilated masks
self._mask_cache = {}
def _get_dilated_mask(self, T, device):
"""Create or retrieve cached dilated attention mask."""
key = (T, self.dilation, self.window_size, device)
if key not in self._mask_cache:
positions = torch.arange(T, device=device)
diff = positions.unsqueeze(1) - positions.unsqueeze(0) # (T, T)
mask = torch.zeros(T, T, dtype=torch.bool, device=device)
for w in range(-self.window_size, self.window_size + 1):
mask |= (diff == w * self.dilation)
self._mask_cache[key] = mask
return self._mask_cache[key]
def forward(self, x, cross_kv=None):
# x: (B, T, D)
B, T, D = x.shape
residual = x
x = self.norm(x)
if cross_kv is not None:
q = self.qkv(x)[:, :, :D] # only use Q from x
kv = self.qkv(cross_kv)[:, :, D:] # K, V from cross_kv
q = q.view(B, T, self.num_heads, self.head_dim).transpose(1, 2)
k = kv[:, :, :D].view(B, T, self.num_heads, self.head_dim).transpose(1, 2)
v = kv[:, :, D:].view(B, T, self.num_heads, self.head_dim).transpose(1, 2)
else:
qkv = self.qkv(x).view(B, T, 3, self.num_heads, self.head_dim)
qkv = qkv.permute(2, 0, 3, 1, 4) # (3, B, H, T, head_dim)
q, k, v = qkv[0], qkv[1], qkv[2]
scale = self.head_dim ** -0.5
attn = (q @ k.transpose(-2, -1)) * scale # (B, H, T, T)
# Apply dilated attention mask
dilated_mask = self._get_dilated_mask(T, x.device) # (T, T)
attn = attn.masked_fill(~dilated_mask.unsqueeze(0).unsqueeze(0), float('-inf'))
attn = F.softmax(attn, dim=-1)
attn = self.dropout(attn)
out = (attn @ v).transpose(1, 2).reshape(B, T, D)
out = self.out_proj(out)
return residual + self.dropout(out)
class ASFormerEncoderBlock(nn.Module):
"""Single encoder block: dilated self-attention + conv feed-forward."""
def __init__(self, d_model, dilation, num_heads=1, kernel_size=3,
dropout=0.1, window_size=5):
super().__init__()
self.self_attn = DilatedAttention(d_model, dilation, num_heads, dropout, window_size)
self.ffn = ConvFeedForward(d_model, kernel_size, dropout)
def forward(self, x):
x = self.self_attn(x)
x = self.ffn(x)
return x
class ASFormerDecoderBlock(nn.Module):
"""Single decoder block: self-attention + cross-attention + conv feed-forward."""
def __init__(self, d_model, dilation, num_heads=1, kernel_size=3,
dropout=0.1, window_size=5):
super().__init__()
self.self_attn = DilatedAttention(d_model, dilation, num_heads, dropout, window_size)
self.cross_attn = DilatedAttention(d_model, dilation, num_heads, dropout, window_size)
self.ffn = ConvFeedForward(d_model, kernel_size, dropout)
def forward(self, x, enc_features):
x = self.self_attn(x)
x = self.cross_attn(x, cross_kv=enc_features)
x = self.ffn(x)
return x
class ASFormerEncoder(nn.Module):
"""ASFormer encoder: projection + N dilated attention layers + output head."""
def __init__(self, input_dim, d_model, num_classes, num_layers=5,
num_heads=1, kernel_size=3, dropout=0.1, window_size=5):
super().__init__()
self.input_proj = nn.Conv1d(input_dim, d_model, 1)
self.pos_enc = PositionalEncoding1D(d_model, dropout)
self.layers = nn.ModuleList([
ASFormerEncoderBlock(d_model, 2 ** i, num_heads, kernel_size, dropout, window_size)
for i in range(num_layers)
])
self.output_proj = nn.Conv1d(d_model, num_classes, 1)
def forward(self, x):
# x: (B, T, C)
x = x.permute(0, 2, 1) # (B, C, T)
x = self.input_proj(x) # (B, d_model, T)
x = x.permute(0, 2, 1) # (B, T, d_model)
x = self.pos_enc(x)
for layer in self.layers:
x = layer(x)
features = x
logits = self.output_proj(x.permute(0, 2, 1)).permute(0, 2, 1) # (B, T, num_classes)
return features, logits
class ASFormerDecoder(nn.Module):
"""ASFormer decoder: refinement stage with cross-attention to encoder."""
def __init__(self, input_dim, d_model, num_classes, num_layers=5,
num_heads=1, kernel_size=3, dropout=0.1, window_size=5):
super().__init__()
self.input_proj = nn.Conv1d(input_dim, d_model, 1)
self.pos_enc = PositionalEncoding1D(d_model, dropout)
self.layers = nn.ModuleList([
ASFormerDecoderBlock(d_model, 2 ** i, num_heads, kernel_size, dropout, window_size)
for i in range(num_layers)
])
self.output_proj = nn.Conv1d(d_model, num_classes, 1)
def forward(self, dec_input, enc_features):
# dec_input: (B, T, input_dim), enc_features: (B, T, d_model)
x = dec_input.permute(0, 2, 1)
x = self.input_proj(x)
x = x.permute(0, 2, 1)
x = self.pos_enc(x)
for layer in self.layers:
x = layer(x, enc_features)
logits = self.output_proj(x.permute(0, 2, 1)).permute(0, 2, 1)
return x, logits
class ASFormer(nn.Module):
"""ASFormer: Transformer for Action Segmentation (Yi et al., BMVC 2021).
Multi-stage encoder-decoder transformer for frame-level action segmentation.
Returns a list of per-stage logits for multi-stage training (same interface as MSTCN).
Args:
input_dim: Input feature dimension
num_classes: Number of action classes
hidden_dim: Hidden dimension (d_model)
num_layers: Number of attention layers per stage (dilation 1, 2, ..., 2^(num_layers-1))
num_decoders: Number of decoder (refinement) stages
num_heads: Number of attention heads
kernel_size: Feed-forward convolution kernel size
dropout: Dropout rate
window_size: Dilated attention window size
"""
def __init__(self, input_dim, num_classes, hidden_dim=64, num_layers=5,
num_decoders=3, num_heads=1, kernel_size=3, dropout=0.1,
window_size=5):
super().__init__()
self.encoder = ASFormerEncoder(
input_dim, hidden_dim, num_classes, num_layers,
num_heads, kernel_size, dropout, window_size
)
self.decoders = nn.ModuleList([
ASFormerDecoder(
num_classes, hidden_dim, num_classes, num_layers,
num_heads, kernel_size, dropout, window_size
) for _ in range(num_decoders)
])
def forward(self, x):
# x: (B, T, C)
outputs = []
enc_features, enc_logits = self.encoder(x)
outputs.append(enc_logits)
for decoder in self.decoders:
dec_input = F.softmax(outputs[-1], dim=-1).detach()
_, dec_logits = decoder(dec_input, enc_features)
outputs.append(dec_logits)
return outputs # list of (B, T, num_classes), compatible with MSTCN interface
class ASFormerContact(nn.Module):
"""ASFormer adapted for binary contact detection (Exp 3).
Wraps ASFormer to return only the final stage output (B, T, 2),
compatible with the exp3 training loop.
Uses multi-stage training internally but returns single output.
"""
def __init__(self, input_dim, hidden_dim=64, num_layers=5, num_decoders=2,
num_heads=1, dropout=0.1):
super().__init__()
self.asformer = ASFormer(
input_dim, num_classes=2, hidden_dim=hidden_dim,
num_layers=num_layers, num_decoders=num_decoders,
num_heads=num_heads, dropout=dropout
)
def forward(self, x):
# x: (B, T, C) -> (B, T, 2)
outputs = self.asformer(x)
return outputs[-1] # Return final stage only
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