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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=88):
# seq_ = seq_.squeeze()
seq_len = seq_.shape[0]
num_nodes = seq_.shape[1]
V = np.zeros((seq_len, num_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()
return V
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()
# return nodes_
def closer_to_zero(current, new_v):
dec = min([(abs(current), current), (abs(new_v), new_v)])[1]
if dec != current:
return True
else:
return False
def batch_bivariate_loss(V_pred, V_trgt, mask=None):
"""
V_pred, V_trgt:
[Batch, Seq_len, Nodes, 5/2];
"""
# mux, muy, sx, sy, corr
# assert V_pred.shape == V_trgt.shape
normx = V_trgt[:, :, :, 0] - V_pred[:, :, :, 0]
normy = V_trgt[:, :, :, 1] - V_pred[:, :, :, 1]
sx = torch.exp(V_pred[:, :, :, 2]) # sx
sy = torch.exp(V_pred[:, :, :, 3]) # sy
# sx = V_pred[:, :, :, 2] # sx
# sy = V_pred[:, :, :, 3] # sy
corr = torch.tanh(V_pred[:, :, :, 4]) # corr
sxsy = sx * sy
z = (normx / sx) ** 2 + (normy / sy) ** 2 - 2 * ((corr * normx * normy) / sxsy)
negRho = 1 - corr ** 2
# Numerator
result = torch.exp(-z / (2 * negRho))
# Normalization factor
denom = 2 * np.pi * (sxsy * torch.sqrt(negRho))
# Final PDF calculation
result = result / denom
# Numerical stability
epsilon = 1e-20
# mask out unrelated nodes
result = torch.einsum("ntv, ntv->ntv", result, mask)
result[mask == 0] = 1.0 # To make the log value of padded nodes as zero;
# total number
n = torch.sum(mask)
result = -torch.log(torch.clamp(result, min=epsilon))
result = result.sum() * 1.0 / n
return result
def bivariate_loss(V_pred, V_trgt):
# mux, muy, sx, sy, corr
# assert V_pred.shape == V_trgt.shape
normx = V_trgt[:, :, 0] - V_pred[:, :, 0]
normy = V_trgt[:, :, 1] - V_pred[:, :, 1]
sx = torch.exp(V_pred[:, :, 2]) # sx
sy = torch.exp(V_pred[:, :, 3]) # sy
corr = torch.tanh(V_pred[:, :, 4]) # corr
sxsy = sx * sy
z = (normx / sx) ** 2 + (normy / sy) ** 2 - 2 * ((corr * normx * normy) / sxsy)
negRho = 1 - corr ** 2
# Numerator
result = torch.exp(-z / (2 * negRho))
# Normalization factor
denom = 2 * np.pi * (sxsy * torch.sqrt(negRho))
# Final PDF calculation
result = result / denom
# Numerical stability
epsilon = 1e-20
result = -torch.log(torch.clamp(result, min=epsilon))
result = torch.mean(result)
return result