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helper_KITTI.py
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helper_KITTI.py
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import numpy as np
import torch
from torch.autograd import Variable
other=[[[0,0],[0,0]],[[0,0],[0,0]],[[0,0],[0,0]],[[24.6152,-17.5380],[40.0037,3.4277]],[[14.2367,- 9.4110],[28.3571,2.6694]],[[0,0],[0,0]]]
def get_mean_error(ret_nodes, nodes, assumedNodesPresent, trueNodesPresent,H,test_dataset):
'''
Computes average displacement error
Parameters
==========
ret_nodes : A tensor of shape pred_length x numNodes x 2
Contains the predicted positions for the nodes
nodes : A tensor of shape pred_length x numNodes x 2
Contains the true positions for the nodes
nodesPresent : A list of lists, of size pred_length
Each list contains the nodeIDs of the nodes present at that time-step
Returns
=======
Error : Mean euclidean distance between predicted trajectory and the true trajectory
'''
pred_length = ret_nodes.size()[0]
error = torch.zeros(pred_length).cuda()
counter = 0
# if test_dataset == 0:
# width = 640
# height = 480
# else:
# width = 720
# height = 576
for tstep in range(pred_length):
for nodeID in assumedNodesPresent:
if nodeID not in trueNodesPresent[tstep]:
continue
pred_pos = ret_nodes[tstep, nodeID, :]
true_pos = nodes[tstep, nodeID, :]
print('pred_pos={}'.format(pred_pos))
print('true_pos={}'.format(true_pos))
true_pos_temp = torch.cuda.FloatTensor(2)
pred_pos_temp = torch.cuda.FloatTensor(2)
# Z
pred_pos_temp[1] = (pred_pos[1] + 1) * (other[test_dataset][1][0] * 0.5) + other[test_dataset][1][1]
# print('other[test_dataset][1][0]',other[test_dataset][1][0])
# X
pred_pos_temp[0] = (pred_pos[0] + 1) * (other[test_dataset][0][0] * 0.5)+other[test_dataset][0][1]
# Z
true_pos_temp[1] = (true_pos[1] + 1) * (other[test_dataset][1][0] * 0.5) + other[test_dataset][1][1]
# X
true_pos_temp[0] = (true_pos[0] + 1) * (other[test_dataset][0][0] * 0.5) + other[test_dataset][0][1]
print('pred_pos_temp={}'.format(pred_pos_temp))
print('true_pos_temp={}'.format(true_pos_temp))
error[tstep] += torch.norm(pred_pos_temp - true_pos_temp, p=2)
counter += 1
if counter != 0:
error[tstep] = error[tstep] / counter
return torch.mean(error)
def get_final_error(ret_nodes, nodes, assumedNodesPresent, trueNodesPresent,H,test_dataset):
'''
Computes final displacement error
Parameters
==========
ret_nodes : A tensor of shape pred_length x numNodes x 2
Contains the predicted positions for the nodes
nodes : A tensor of shape pred_length x numNodes x 2
Contains the true positions for the nodes
nodesPresent : A list of lists, of size pred_length
Each list contains the nodeIDs of the nodes present at that time-step
Returns
=======
Error : Mean final euclidean distance between predicted trajectory and the true trajectory
'''
pred_length = ret_nodes.size()[0]
error = 0
counter = 0
# if test_dataset==0:
# width=640
# height=480
# else:
# width=720
# height=576
# Last time-step
tstep = pred_length - 1
for nodeID in assumedNodesPresent:
if nodeID not in trueNodesPresent[tstep]:
continue
pred_pos = ret_nodes[tstep, nodeID, :]
true_pos = nodes[tstep, nodeID, :]
print('pred_pos={}'.format(pred_pos))
print('true_pos={}'.format(true_pos))
true_pos_temp = torch.cuda.FloatTensor(2)
pred_pos_temp = torch.cuda.FloatTensor(2)
# Z
pred_pos_temp[1] = (pred_pos[1] + 1) * (other[test_dataset][1][0] * 0.5) + other[test_dataset][1][1]
# X
pred_pos_temp[0] = (pred_pos[0] + 1) * (other[test_dataset][0][0] * 0.5) + other[test_dataset][0][1]
# Z
true_pos_temp[1] = (true_pos[1] + 1) * (other[test_dataset][1][0] * 0.5) + other[test_dataset][1][1]
# X
true_pos_temp[0] = (true_pos[0] + 1) * (other[test_dataset][0][0] * 0.5) + other[test_dataset][0][1]
print('pred_pos_temp={}'.format(pred_pos_temp))
print('true_pos_temp={}'.format(true_pos_temp))
error += torch.norm(pred_pos_temp - true_pos_temp, p=2)
counter += 1
if counter != 0:
error = error / counter
return error