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metrics.py
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metrics.py
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import os
import math
import sys
import torch
import torch.nn as nn
import numpy as np
import torch.nn.functional as Func
from torch.nn import init
from torch.nn.parameter import Parameter
from torch.nn.modules.module import Module
import torch.optim as optim
from torch.utils.data import Dataset
from torch.utils.data import DataLoader
from numpy import linalg as LA
import networkx as nx
def ade(predAll, targetAll, count_):
All = len(predAll)
sum_all = 0
for s in range(All):
pred = np.swapaxes(predAll[s][:, :count_[s], :], 0, 1)
target = np.swapaxes(targetAll[s][:, :count_[s], :], 0, 1)
N = pred.shape[0]
T = pred.shape[1]
sum_ = 0
for i in range(N):
for t in range(T):
sum_ += math.sqrt((pred[i, t, 0] - target[i, t, 0])**2 +
(pred[i, t, 1] - target[i, t, 1])**2)
sum_all += sum_ / (N * T)
return sum_all / All
def fde(predAll, targetAll, count_):
All = len(predAll)
sum_all = 0
for s in range(All):
pred = np.swapaxes(predAll[s][:, :count_[s], :], 0, 1)
target = np.swapaxes(targetAll[s][:, :count_[s], :], 0, 1)
N = pred.shape[0]
T = pred.shape[1]
sum_ = 0
for i in range(N):
for t in range(T - 1, T):
sum_ += math.sqrt((pred[i, t, 0] - target[i, t, 0])**2 +
(pred[i, t, 1] - target[i, t, 1])**2)
sum_all += sum_ / (N)
return sum_all / All
def seq_to_nodes(seq_):
max_nodes = seq_.shape[1] #number of pedestrians in the graph
seq_ = seq_.squeeze()
seq_len = seq_.shape[2]
V = np.zeros((seq_len, max_nodes, 2))
for s in range(seq_len):
step_ = seq_[:, :, s]
for h in range(len(step_)):
V[s, h, :] = step_[h]
return V.squeeze()
def nodes_rel_to_nodes_abs(nodes, init_node):
nodes_ = np.zeros_like(nodes)
for s in range(nodes.shape[0]):
for ped in range(nodes.shape[1]):
nodes_[s, ped, :] = np.sum(nodes[:s + 1, ped, :],
axis=0) + init_node[ped, :]
return nodes_.squeeze()