DeepLearningExamples/DGLPyTorch/DrugDiscovery/SE3Transformer/tests/utils.py

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import dgl
import torch


def get_random_graph(N, num_edges_factor=18):
    graph = dgl.transform.remove_self_loop(dgl.rand_graph(N, N * num_edges_factor))
    return graph


def assign_relative_pos(graph, coords):
    src, dst = graph.edges()
    graph.edata['rel_pos'] = coords[src] - coords[dst]
    return graph


def get_max_diff(a, b):
    return (a - b).abs().max().item()


def rot_z(gamma):
    return torch.tensor([
        [torch.cos(gamma), -torch.sin(gamma), 0],
        [torch.sin(gamma), torch.cos(gamma), 0],
        [0, 0, 1]
    ], dtype=gamma.dtype)


def rot_y(beta):
    return torch.tensor([
        [torch.cos(beta), 0, torch.sin(beta)],
        [0, 1, 0],
        [-torch.sin(beta), 0, torch.cos(beta)]
    ], dtype=beta.dtype)


def rot(alpha, beta, gamma):
    return rot_z(alpha) @ rot_y(beta) @ rot_z(gamma)