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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 VertexDirectEmbedder(nn.Module):
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"""
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Class responsible for embedding vertices. Vertex embeddings take
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the form of a tensor of size [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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"""
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def __init__(self, num_vertices: int, embed_dim: int):
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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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embed_dim (int): number of dimensions in the embedding space
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"""
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super(VertexDirectEmbedder, self).__init__()
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self.embeddings = nn.Parameter(torch.Tensor(num_vertices, 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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"""
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Reset embeddings to random values
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"""
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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(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 ["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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