(self, input_dim, latent_dim, ff_size, num_layers, num_heads,
dropout, activation)
| 37 | class MotionEncoder(nn.Module): |
| 38 | |
| 39 | def __init__(self, input_dim, latent_dim, ff_size, num_layers, num_heads, |
| 40 | dropout, activation): |
| 41 | super().__init__() |
| 42 | |
| 43 | self.input_feats = input_dim |
| 44 | self.latent_dim = latent_dim |
| 45 | self.ff_size = ff_size |
| 46 | self.num_layers = num_layers |
| 47 | self.num_heads = num_heads |
| 48 | self.dropout = dropout |
| 49 | self.activation = activation |
| 50 | |
| 51 | self.query_token = nn.Parameter(torch.randn(1, self.latent_dim)) |
| 52 | |
| 53 | self.embed_motion = nn.Linear(self.input_feats * 2, self.latent_dim) |
| 54 | self.sequence_pos_encoder = PositionalEncoding(self.latent_dim, |
| 55 | self.dropout, |
| 56 | max_len=2000) |
| 57 | |
| 58 | seqTransEncoderLayer = nn.TransformerEncoderLayer( |
| 59 | d_model=self.latent_dim, |
| 60 | nhead=self.num_heads, |
| 61 | dim_feedforward=self.ff_size, |
| 62 | dropout=self.dropout, |
| 63 | activation=self.activation) |
| 64 | self.transformer = nn.TransformerEncoder(seqTransEncoderLayer, |
| 65 | num_layers=self.num_layers) |
| 66 | self.out_ln = nn.LayerNorm(self.latent_dim) |
| 67 | self.out = nn.Linear(self.latent_dim, 512) |
| 68 | |
| 69 | def forward(self, motion, motion_mask): |
| 70 | x, mask = motion, motion_mask |
nothing calls this directly
no test coverage detected