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import networkx as nx


def to_directed(graph):
    G = nx.DiGraph(source=[], sink=[])
    for node in range(graph.number_of_nodes()):
        neighbors = list(graph.neighbors(node))
        neighbors.sort()
        if node < neighbors[0]:  # input nodes
            G.graph['source'].append(node)

bhpfelix

import numpy as np
np.random.seed(0)
import torch
import torch.nn as nn
import tensorflow as tf

import matplotlib.pyplot as plt

slim = tf.contrib.slim

bhpfelix

import numpy as np
from PIL import Image
np.random.seed(2)
import torchvision
import torch
# torch.manual_seed(0)
import torch.nn as nn
import torch.nn.functional as F
from torchvision import transforms
import tensorflow as tf

bhpfelix

import numpy as np
from numpy.linalg import norm
from scipy.special import erf


def get_rotation_matrix(x):
    """
    Get rotation matrix for the space that aligns vector x with y = [1, 0, 0, 0, 0, ..., 0] 
    See: https://math.stackexchange.com/questions/598750/finding-the-rotation-matrix-in-n-dimensions
    """

bhpfelix

conda create -n habitat_headless python=3.6 cmake=3.14.0

conda activate habitat_headless

cd ~/github/habitat-sim/

pip install -r requirements.txt

conda activate habitat_headless

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import torch
import numpy as np

# This will be share by both iterations and will make the second backward fail !
a = torch.ones(3, requires_grad=True) * 4

# Instead, do
# a = torch.tensor(np.ones(3) * 4, requires_grad=True)

for i in range(10):