Datasets:
jblitzar
/
github-python-corpus

Dataset card Data Studio Files
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plt.xticks(rotation=45)
plt.ylabel("risk")
if not os.path.exists('plots'):
os.mkdir('plots')
fig_name = 'plots/tournament_risks.png'
fig.savefig(fig_name, dpi=fig.dpi)
print('Plot of portfolio risk over time has been saved in {}/{}.'.format(os.getcwd(), fig_name))
# <FILESEP>
# Copyright Niantic 2019. Patent Pending. All rights reserved.
#
# This software is licensed under the terms of the Monodepth2 licence
# which allows for non-commercial use only, the full terms of which are made
# available in the LICENSE file.
from __future__ import absolute_import, division, print_function
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
def disp_to_depth(disp, min_depth, max_depth):
"""Convert network's sigmoid output into depth prediction
The formula for this conversion is given in the 'additional considerations'
section of the paper.
"""
min_disp = 1 / max_depth
max_disp = 1 / min_depth
scaled_disp = min_disp + (max_disp - min_disp) * disp
depth = 1 / scaled_disp
return scaled_disp, depth
def transformation_from_parameters(axisangle, translation, invert=False):
"""Convert the network's (axisangle, translation) output into a 4x4 matrix"""
R = rot_from_axisangle(axisangle)
t = translation.clone()
if invert:
R = R.transpose(1, 2)
t *= -1
T = get_translation_matrix(t)
M = torch.matmul(R, T) if invert else torch.matmul(T, R)
return M
def get_translation_matrix(translation_vector):
"""Convert a translation vector into a 4x4 transformation matrix"""
T = torch.zeros(translation_vector.shape[0], 4, 4).to(device=translation_vector.device)
t = translation_vector.contiguous().view(-1, 3, 1)
T[:, 0, 0] = 1
T[:, 1, 1] = 1
T[:, 2, 2] = 1
T[:, 3, 3] = 1
T[:, :3, 3, None] = t
return T
def rot_from_axisangle(vec):
"""Convert an axisangle rotation into a 4x4 transformation matrix
(adapted from https://github.com/Wallacoloo/printipi)
Input 'vec' has to be Bx1x3
"""
angle = torch.norm(vec, 2, 2, True)
axis = vec / (angle + 1e-7)
ca = torch.cos(angle)
sa = torch.sin(angle)
C = 1 - ca
x = axis[..., 0].unsqueeze(1)
y = axis[..., 1].unsqueeze(1)
z = axis[..., 2].unsqueeze(1)
xs = x * sa
ys = y * sa
zs = z * sa
xC = x * C
yC = y * C
zC = z * C
xyC = x * yC
yzC = y * zC
zxC = z * xC
rot = torch.zeros((vec.shape[0], 4, 4)).to(device=vec.device)
rot[:, 0, 0] = torch.squeeze(x * xC + ca)
rot[:, 0, 1] = torch.squeeze(xyC - zs)
rot[:, 0, 2] = torch.squeeze(zxC + ys)
rot[:, 1, 0] = torch.squeeze(xyC + zs)