| |
|
|
| import pickle |
| import torch |
| from torch import nn |
|
|
| from detectron2.utils.file_io import PathManager |
|
|
| from .utils import normalize_embeddings |
|
|
|
|
| class VertexFeatureEmbedder(nn.Module): |
| """ |
| Class responsible for embedding vertex features. Mapping from |
| feature space to the embedding space is a tensor of size [K, D], where |
| K = number of dimensions in the feature space |
| D = number of dimensions in the embedding space |
| Vertex features is a tensor of size [N, K], where |
| N = number of vertices |
| K = number of dimensions in the feature space |
| Vertex embeddings are computed as F * E = tensor of size [N, D] |
| """ |
|
|
| def __init__( |
| self, num_vertices: int, feature_dim: int, embed_dim: int, train_features: bool = False |
| ): |
| """ |
| Initialize embedder, set random embeddings |
| |
| Args: |
| num_vertices (int): number of vertices to embed |
| feature_dim (int): number of dimensions in the feature space |
| embed_dim (int): number of dimensions in the embedding space |
| train_features (bool): determines whether vertex features should |
| be trained (default: False) |
| """ |
| super(VertexFeatureEmbedder, self).__init__() |
| if train_features: |
| self.features = nn.Parameter(torch.Tensor(num_vertices, feature_dim)) |
| else: |
| self.register_buffer("features", torch.Tensor(num_vertices, feature_dim)) |
| self.embeddings = nn.Parameter(torch.Tensor(feature_dim, embed_dim)) |
| self.reset_parameters() |
|
|
| @torch.no_grad() |
| def reset_parameters(self): |
| self.features.zero_() |
| self.embeddings.zero_() |
|
|
| def forward(self) -> torch.Tensor: |
| """ |
| Produce vertex embeddings, a tensor of shape [N, D] where: |
| N = number of vertices |
| D = number of dimensions in the embedding space |
| |
| Return: |
| Full vertex embeddings, a tensor of shape [N, D] |
| """ |
| return normalize_embeddings(torch.mm(self.features, self.embeddings)) |
|
|
| @torch.no_grad() |
| def load(self, fpath: str): |
| """ |
| Load data from a file |
| |
| Args: |
| fpath (str): file path to load data from |
| """ |
| with PathManager.open(fpath, "rb") as hFile: |
| data = pickle.load(hFile) |
| for name in ["features", "embeddings"]: |
| if name in data: |
| getattr(self, name).copy_( |
| torch.tensor(data[name]).float().to(device=getattr(self, name).device) |
| ) |
|
|
