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import torch.nn as nn
import torch.nn.functional as F
class CustomLSTM(nn.Module):
def __init__(self, input_size, hidden_size, num_classes):
super(CustomLSTM, self).__init__()
self.lstm1 = nn.LSTM(input_size, hidden_size, batch_first=True)
self.lstm2 = nn.LSTM(hidden_size, hidden_size, batch_first=True)
self.lstm3 = nn.LSTM(hidden_size, hidden_size, batch_first=True)
self.fc1 = nn.Linear(hidden_size, 64)
self.fc2 = nn.Linear(64, 128)
self.fc3 = nn.Linear(128, 64)
self.fc4 = nn.Linear(64, 32)
self.fc5 = nn.Linear(32, 32)
self.output_layer = nn.Linear(32, num_classes)
def forward(self, x):
x, _ = self.lstm1(x)
x, _ = self.lstm2(x)
x, _ = self.lstm3(x)
x = torch.relu(self.fc1(x[:, -1, :]))
x = torch.relu(self.fc2(x))
x = torch.relu(self.fc3(x))
x = torch.relu(self.fc4(x))
x = torch.relu(self.fc5(x))
x = self.output_layer(x)
return x
class STGCNLayer(nn.Module):
def __init__(self, in_channels, out_channels, adjacency_matrix, dropout=0.2):
super(STGCNLayer, self).__init__()
self.A = nn.Parameter(torch.from_numpy(adjacency_matrix).float(), requires_grad=False)
self.gcn = nn.Conv2d(in_channels, out_channels, kernel_size=1)
self.tcn = nn.Sequential(
nn.BatchNorm2d(out_channels),
nn.ReLU(),
nn.Conv2d(out_channels, out_channels, kernel_size=(3, 1), padding=(1, 0)),
nn.BatchNorm2d(out_channels),
nn.Dropout(dropout),
nn.ReLU()
)
def forward(self, x):
x = torch.einsum('nctv,vw->nctw', x, self.A)
x = self.gcn(x)
x = self.tcn(x)
return x
class STGCNModel(nn.Module):
def __init__(self, num_classes, adjacency_matrix):
super(STGCNModel, self).__init__()
self.layer1 = STGCNLayer(3, 64, adjacency_matrix)
self.layer2 = STGCNLayer(64, 128, adjacency_matrix)
self.layer3 = STGCNLayer(128, 256, adjacency_matrix)
self.drop = nn.Dropout(0.2)
self.fc = nn.Linear(256, num_classes)
def forward(self, x):
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
x = F.avg_pool2d(x, x.size()[2:])
x = x.view(x.size(0), -1)
x = self.drop(x)
return self.fc(x)
class CTRGC(nn.Module):
def __init__(self, in_channels, out_channels, rel_reduction=8):
super(CTRGC, self).__init__()
self.in_channels = in_channels
self.out_channels = out_channels
self.rel_channels = max(1, in_channels // rel_reduction)
self.conv_1 = nn.Conv2d(self.in_channels, self.rel_channels, kernel_size=1)
self.conv_2 = nn.Conv2d(self.in_channels, self.rel_channels, kernel_size=1)
self.conv_3 = nn.Conv2d(self.in_channels, self.out_channels, kernel_size=1)
self.conv_4 = nn.Conv2d(self.rel_channels, self.out_channels, kernel_size=1)
self.tanh = nn.Tanh()
def forward(self, x, A):
# x: (N, C, T, V), A: (V, V)
x1 = self.conv_1(x).mean(-2) # (N, C_rel, V)
x2 = self.conv_2(x).mean(-2) # (N, C_rel, V)
# Channel-wise topology refinement
x1 = x1.unsqueeze(3) # (N, C_rel, V, 1)
x2 = x2.unsqueeze(2) # (N, C_rel, 1, V)
a1 = self.tanh(x1 - x2) # (N, C_rel, V, V)
a2 = self.conv_4(a1) # (N, C_out, V, V)
# Combine with static adjacency matrix
alpha = a2 + A.unsqueeze(0).unsqueeze(0) # (N, C_out, V, V)
x = self.conv_3(x) # (N, C_out, T, V)
# alpha is (N, C, V, W), unsqueeze it to (N, C, 1, V, W)
alpha = alpha.unsqueeze(2)
# Final aggregation
# Now einsum equation 'nctvw,nctw->nctv' matches:
# alpha: (n)batch, (c)channel, (t)1, (v)node, (w)node
# x: (n)batch, (c)channel, (t)time, (w)node
out = torch.einsum('nctvw,nctw->nctv', alpha, x)
return out
class CTRGCNLayer(nn.Module):
def __init__(self, in_channels, out_channels, adjacency_matrix, dropout=0.2):
super(CTRGCNLayer, self).__init__()
self.A = nn.Parameter(torch.from_numpy(adjacency_matrix).float(), requires_grad=False)
self.gc = CTRGC(in_channels, out_channels)
self.tcn = nn.Sequential(
nn.BatchNorm2d(out_channels),
nn.ReLU(),
nn.Conv2d(out_channels, out_channels, kernel_size=(3, 1), padding=(1, 0)),
nn.BatchNorm2d(out_channels),
nn.Dropout(dropout),
nn.ReLU()
)
def forward(self, x):
x = self.gc(x, self.A)
x = self.tcn(x)
return x
class CTRGCNModel(nn.Module):
def __init__(self, num_classes, adjacency_matrix):
super(CTRGCNModel, self).__init__()
self.layer1 = CTRGCNLayer(3, 64, adjacency_matrix)
self.layer2 = CTRGCNLayer(64, 128, adjacency_matrix)
self.layer3 = CTRGCNLayer(128, 256, adjacency_matrix)
self.drop = nn.Dropout(0.2)
self.fc = nn.Linear(256, num_classes)
def forward(self, x):
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
x = F.avg_pool2d(x, x.size()[2:])
x = x.view(x.size(0), -1)
x = self.drop(x)
return self.fc(x)
class FactorizedAttention(nn.Module):
"""
Processes Temporal and Spatial dimensions separately to reduce
complexity from O((T*V)^2) to O(V*T^2 + T*V^2).
"""
def __init__(self, embed_dim, num_heads, dropout=0.1):
super().__init__()
self.temp_attn = nn.MultiheadAttention(embed_dim, num_heads, batch_first=True, dropout=dropout)
self.spat_attn = nn.MultiheadAttention(embed_dim, num_heads, batch_first=True, dropout=dropout)
self.norm1 = nn.LayerNorm(embed_dim)
self.norm2 = nn.LayerNorm(embed_dim)
self.ffn = nn.Sequential(
nn.Linear(embed_dim, embed_dim * 2),
nn.ReLU(),
nn.Linear(embed_dim * 2, embed_dim)
)
self.norm3 = nn.LayerNorm(embed_dim)
def forward(self, x, T, V):
# x shape: (N, T, V, C)
N, _, _, C = x.shape
# 1. Temporal Attention: (N*V, T, C)
xt = x.permute(0, 2, 1, 3).reshape(N * V, T, C)
attn_t, _ = self.temp_attn(xt, xt, xt)
x = x + attn_t.view(N, V, T, C).permute(0, 2, 1, 3)
x = self.norm1(x)
# 2. Spatial Attention: (N*T, V, C)
xs = x.reshape(N * T, V, C)
attn_s, _ = self.spat_attn(xs, xs, xs)
x = x + attn_s.view(N, T, V, C)
x = self.norm2(x)
# 3. Feed Forward
x = x + self.ffn(x)
x = self.norm3(x)
return x
class SkateFormerModel(nn.Module):
def __init__(self, num_classes, num_joints=75, seq_len=30, embed_dim=128, num_heads=8):
super(SkateFormerModel, self).__init__()
self.T = seq_len
self.V = num_joints
self.input_embed = nn.Linear(3, embed_dim)
# Position embedding for Time and Space
self.temp_embed = nn.Parameter(torch.zeros(1, seq_len, 1, embed_dim))
self.spat_embed = nn.Parameter(torch.zeros(1, 1, num_joints, embed_dim))
# Two factorized layers are usually enough for small datasets
self.layer1 = FactorizedAttention(embed_dim, num_heads)
self.layer2 = FactorizedAttention(embed_dim, num_heads)
self.fc = nn.Sequential(
nn.Linear(embed_dim, 256),
nn.ReLU(),
nn.Dropout(0.3),
nn.Linear(256, num_classes)
)
def forward(self, x):
# x: (N, 3, T, V)
N, C, T, V = x.shape
x = x.permute(0, 2, 3, 1) # (N, T, V, 3)
x = self.input_embed(x)
x = x + self.temp_embed + self.spat_embed
x = self.layer1(x, T, V)
x = self.layer2(x, T, V)
# Global Average Pool
x = x.mean(dim=[1, 2])
return self.fc(x)
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