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