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utils.py
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utils.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
from tqdm import tqdm
import time
import pickle
def anorm(p1, p2):
NORM = math.sqrt((p1[0] - p2[0])**2 + (p1[1] - p2[1])**2)
if NORM == 0:
return 0
return 1 / (NORM)
def seq_to_graph(seq_, seq_rel, norm_lap_matr=True):
seq_ = seq_.squeeze()
seq_rel = seq_rel.squeeze()
seq_len = seq_.shape[2]
max_nodes = seq_.shape[0]
V = np.zeros((seq_len, max_nodes, 2))
A = np.zeros((seq_len, max_nodes, max_nodes))
for s in range(seq_len):
step_ = seq_[:, :, s]
step_rel = seq_rel[:, :, s]
for h in range(len(step_)):
V[s, h, :] = step_rel[h]
A[s, h, h] = 1
for k in range(h + 1, len(step_)):
l2_norm = anorm(step_rel[h], step_rel[k])
A[s, h, k] = l2_norm
A[s, k, h] = l2_norm
if norm_lap_matr:
G = nx.from_numpy_array(A[s, :, :])
A[s, :, :] = nx.normalized_laplacian_matrix(G).toarray()
# Create Local graphs
return torch.from_numpy(V).type(torch.double),\
torch.from_numpy(A).type(torch.double)
def poly_fit(traj, traj_len, threshold):
"""
Input:
- traj: Numpy array of shape (2, traj_len)
- traj_len: Len of trajectory
- threshold: Minimum error to be considered for non linear traj
Output:
- int: 1 -> Non Linear 0-> Linear
"""
t = np.linspace(0, traj_len - 1, traj_len)
res_x = np.polyfit(t, traj[0, -traj_len:], 2, full=True)[1]
res_y = np.polyfit(t, traj[1, -traj_len:], 2, full=True)[1]
if res_x + res_y >= threshold:
return 1.0
else:
return 0.0
def read_file(_path, delim='\t'):
data = []
if delim == 'tab':
delim = '\t'
elif delim == 'space':
delim = ' '
with open(_path, 'r') as f:
for line in f:
line = line.strip().split(delim)
line = [float(i) for i in line]
data.append(line)
return np.asarray(data)
class TrajectoryDataset(Dataset):
"""Dataloder for the Trajectory datasets"""
def __init__(self,
data_dir,
obs_len=8,
pred_len=8,
skip=1,
threshold=0.002,
min_ped=1,
delim='\t',
norm_lap_matr=True):
"""
Args:
- data_dir: Directory containing dataset files in the format
<frame_id> <ped_id> <x> <y>
- obs_len: Number of time-steps in input trajectories
- pred_len: Number of time-steps in output trajectories
- skip: Number of frames to skip while making the dataset
- threshold: Minimum error to be considered for non linear traj
when using a linear predictor
- min_ped: Minimum number of pedestrians that should be in a seqeunce
- delim: Delimiter in the dataset files
"""
super(TrajectoryDataset, self).__init__()
self.max_peds_in_frame = 0
self.data_dir = data_dir
self.obs_len = obs_len
self.pred_len = pred_len
self.skip = skip
self.seq_len = self.obs_len + self.pred_len
self.delim = delim
self.norm_lap_matr = norm_lap_matr
args_str = data_dir + str(obs_len) + str(pred_len) + str(skip) + \
str(threshold) + str(min_ped) + str(norm_lap_matr)
pkl_path = './pkls/' + args_str.replace("/", "_") + '.pkl'
if os.path.exists(pkl_path):
print("Dataset found, Loading dataset from:", pkl_path)
with open(pkl_path, 'rb') as f:
__data = pickle.load(f)
self.obs_traj = __data["obs_traj"]
self.pred_traj = __data["pred_traj"]
self.obs_traj_rel = __data["obs_traj_rel"]
self.pred_traj_rel = __data["pred_traj_rel"]
self.non_linear_ped = __data["non_linear_ped"]
self.loss_mask = __data["loss_mask"]
self.v_obs = __data["v_obs"]
self.A_obs = __data["A_obs"]
self.v_pred = __data["v_pred"]
self.A_pred = __data["A_pred"]
self.num_seq = __data["num_seq"]
self.seq_start_end = __data["seq_start_end"]
else:
all_files = os.listdir(self.data_dir)
all_files = [
os.path.join(self.data_dir, _path) for _path in all_files
]
num_peds_in_seq = []
seq_list = []
seq_list_rel = []
loss_mask_list = []
non_linear_ped = []
for path in all_files:
data = read_file(path, delim)
frames = np.unique(data[:, 0]).tolist()
frame_data = []
for frame in frames:
frame_data.append(data[frame == data[:, 0], :])
num_sequences = int(
math.ceil((len(frames) - self.seq_len + 1) / skip))
for idx in range(0, num_sequences * self.skip + 1, skip):
curr_seq_data = np.concatenate(frame_data[idx:idx +
self.seq_len],
axis=0)
peds_in_curr_seq = np.unique(curr_seq_data[:, 1])
self.max_peds_in_frame = max(self.max_peds_in_frame,
len(peds_in_curr_seq))
curr_seq_rel = np.zeros(
(len(peds_in_curr_seq), 2, self.seq_len))
curr_seq = np.zeros(
(len(peds_in_curr_seq), 2, self.seq_len))
curr_loss_mask = np.zeros(
(len(peds_in_curr_seq), self.seq_len))
num_peds_considered = 0
_non_linear_ped = []
for _, ped_id in enumerate(peds_in_curr_seq):
curr_ped_seq = curr_seq_data[curr_seq_data[:, 1] ==
ped_id, :]
# curr_ped_seq = np.around(curr_ped_seq, decimals=4)
pad_front = frames.index(curr_ped_seq[0, 0]) - idx
pad_end = frames.index(curr_ped_seq[-1, 0]) - idx + 1
if pad_end - pad_front != self.seq_len:
continue
curr_ped_seq = np.transpose(curr_ped_seq[:, 2:])
curr_ped_seq = curr_ped_seq
# Make coordinates relative
rel_curr_ped_seq = np.zeros(curr_ped_seq.shape)
rel_curr_ped_seq[:, 1:] = \
curr_ped_seq[:, 1:] - curr_ped_seq[:, :-1]
_idx = num_peds_considered
curr_seq[_idx, :, pad_front:pad_end] = curr_ped_seq
curr_seq_rel[_idx, :,
pad_front:pad_end] = rel_curr_ped_seq
# Linear vs Non-Linear Trajectory
_non_linear_ped.append(
poly_fit(curr_ped_seq, pred_len, threshold))
curr_loss_mask[_idx, pad_front:pad_end] = 1
num_peds_considered += 1
if num_peds_considered > min_ped:
non_linear_ped += _non_linear_ped
num_peds_in_seq.append(num_peds_considered)
loss_mask_list.append(
curr_loss_mask[:num_peds_considered])
seq_list.append(curr_seq[:num_peds_considered])
seq_list_rel.append(curr_seq_rel[:num_peds_considered])
self.num_seq = len(seq_list)
seq_list = np.concatenate(seq_list, axis=0)
seq_list_rel = np.concatenate(seq_list_rel, axis=0)
loss_mask_list = np.concatenate(loss_mask_list, axis=0)
non_linear_ped = np.asarray(non_linear_ped)
# Convert numpy -> Torch Tensor
self.obs_traj = torch.from_numpy(
seq_list[:, :, :self.obs_len]).type(torch.double)
self.pred_traj = torch.from_numpy(
seq_list[:, :, self.obs_len:]).type(torch.double)
self.obs_traj_rel = torch.from_numpy(
seq_list_rel[:, :, :self.obs_len]).type(torch.double)
self.pred_traj_rel = torch.from_numpy(
seq_list_rel[:, :, self.obs_len:]).type(torch.double)
self.loss_mask = torch.from_numpy(loss_mask_list).type(
torch.double)
self.non_linear_ped = torch.from_numpy(non_linear_ped).type(
torch.double)
cum_start_idx = [0] + np.cumsum(num_peds_in_seq).tolist()
self.seq_start_end = [
(start, end)
for start, end in zip(cum_start_idx, cum_start_idx[1:])
]
# Convert to Graphs
self.v_obs = []
self.A_obs = []
self.v_pred = []
self.A_pred = []
print("Processing Data .....")
pbar = tqdm(total=len(self.seq_start_end))
for ss in range(len(self.seq_start_end)):
pbar.update(1)
start, end = self.seq_start_end[ss]
v_, a_ = seq_to_graph(self.obs_traj[start:end, :],
self.obs_traj_rel[start:end, :],
self.norm_lap_matr)
self.v_obs.append(v_.clone())
self.A_obs.append(a_.clone())
v_, a_ = seq_to_graph(self.pred_traj[start:end, :],
self.pred_traj_rel[start:end, :],
self.norm_lap_matr)
self.v_pred.append(v_.clone())
self.A_pred.append(a_.clone())
pbar.close()
__data = {}
__data["obs_traj"] = self.obs_traj
__data["pred_traj"] = self.pred_traj
__data["obs_traj_rel"] = self.obs_traj_rel
__data["pred_traj_rel"] = self.pred_traj_rel
__data["non_linear_ped"] = self.non_linear_ped
__data["loss_mask"] = self.loss_mask
__data["v_obs"] = self.v_obs
__data["A_obs"] = self.A_obs
__data["v_pred"] = self.v_pred
__data["A_pred"] = self.A_pred
__data["num_seq"] = self.num_seq
__data["seq_start_end"] = self.seq_start_end
print("Saving dataset to:", pkl_path)
with open(pkl_path, "wb") as output_file:
pickle.dump(__data, output_file)
def __len__(self):
return self.num_seq
def __getitem__(self, index):
start, end = self.seq_start_end[index]
out = [
self.obs_traj[start:end, :], self.pred_traj[start:end, :],
self.obs_traj_rel[start:end, :], self.pred_traj_rel[start:end, :],
self.non_linear_ped[start:end], self.loss_mask[start:end, :],
self.v_obs[index], self.A_obs[index], self.v_pred[index],
self.A_pred[index]
]
return out
class TrajectoryDatasetEval(Dataset):
"""Dataloder for the Trajectory datasets"""
def __init__(self,
data_dir,
obs_len=8,
pred_len=8,
skip=1,
threshold=0.002,
min_ped=1,
delim='\t',
norm_lap_matr=True):
"""
Args:
- data_dir: Directory containing dataset files in the format
<frame_id> <ped_id> <x> <y>
- obs_len: Number of time-steps in input trajectories
- pred_len: Number of time-steps in output trajectories
- skip: Number of frames to skip while making the dataset
- threshold: Minimum error to be considered for non linear traj
when using a linear predictor
- min_ped: Minimum number of pedestrians that should be in a seqeunce
- delim: Delimiter in the dataset files
"""
super(TrajectoryDatasetEval, self).__init__()
self.max_peds_in_frame = 0
self.data_dir = data_dir
self.obs_len = obs_len
self.pred_len = pred_len
self.skip = skip
self.seq_len = self.obs_len + self.pred_len
self.delim = delim
self.norm_lap_matr = norm_lap_matr
args_str = data_dir + str(obs_len) + str(pred_len) + str(skip) + \
str(threshold) + str(min_ped) + str(norm_lap_matr)
pkl_path = './pkls/' + args_str.replace("/", "_") + '.pkl'
if os.path.exists(pkl_path):
print("Dataset found, Loading dataset from:", pkl_path)
with open(pkl_path, 'rb') as f:
__data = pickle.load(f)
self.obs_traj = __data["obs_traj"]
self.pred_traj = __data["pred_traj"]
self.obs_traj_rel = __data["obs_traj_rel"]
self.pred_traj_rel = __data["pred_traj_rel"]
self.non_linear_ped = __data["non_linear_ped"]
self.loss_mask = __data["loss_mask"]
self.v_obs = __data["v_obs"]
self.A_obs = __data["A_obs"]
self.v_pred = __data["v_pred"]
self.A_pred = __data["A_pred"]
self.num_seq = __data["num_seq"]
self.seq_start_end = __data["seq_start_end"]
else:
all_files = os.listdir(self.data_dir)
all_files = [
os.path.join(self.data_dir, _path) for _path in all_files
]
num_peds_in_seq = []
seq_list = []
seq_list_rel = []
loss_mask_list = []
non_linear_ped = []
for path in all_files:
data = read_file(path, delim)
frames = np.unique(data[:, 0]).tolist()
frame_data = []
for frame in frames:
frame_data.append(data[frame == data[:, 0], :])
num_sequences = int(
math.ceil((len(frames) - self.seq_len + 1) / skip))
for idx in range(0, num_sequences * self.skip + 1, skip):
curr_seq_data = np.concatenate(frame_data[idx:idx +
self.seq_len],
axis=0)
peds_in_curr_seq = np.unique(curr_seq_data[:, 1])
self.max_peds_in_frame = max(self.max_peds_in_frame,
len(peds_in_curr_seq))
curr_seq_rel = np.zeros(
(len(peds_in_curr_seq), 2, self.seq_len))
curr_seq = np.zeros(
(len(peds_in_curr_seq), 2, self.seq_len))
curr_loss_mask = np.zeros(
(len(peds_in_curr_seq), self.seq_len))
num_peds_considered = 0
_non_linear_ped = []
for _, ped_id in enumerate(peds_in_curr_seq):
curr_ped_seq = curr_seq_data[curr_seq_data[:, 1] ==
ped_id, :]
curr_ped_seq = np.around(curr_ped_seq, decimals=4)
pad_front = frames.index(curr_ped_seq[0, 0]) - idx
pad_end = frames.index(curr_ped_seq[-1, 0]) - idx + 1
if pad_end - pad_front != self.seq_len:
continue
curr_ped_seq = np.transpose(curr_ped_seq[:, 2:])
curr_ped_seq = curr_ped_seq
# Make coordinates relative
rel_curr_ped_seq = np.zeros(curr_ped_seq.shape)
rel_curr_ped_seq[:, 1:] = \
curr_ped_seq[:, 1:] - curr_ped_seq[:, :-1]
_idx = num_peds_considered
curr_seq[_idx, :, pad_front:pad_end] = curr_ped_seq
curr_seq_rel[_idx, :,
pad_front:pad_end] = rel_curr_ped_seq
# Linear vs Non-Linear Trajectory
_non_linear_ped.append(
poly_fit(curr_ped_seq, pred_len, threshold))
curr_loss_mask[_idx, pad_front:pad_end] = 1
num_peds_considered += 1
if num_peds_considered > min_ped:
non_linear_ped += _non_linear_ped
num_peds_in_seq.append(num_peds_considered)
loss_mask_list.append(
curr_loss_mask[:num_peds_considered])
seq_list.append(curr_seq[:num_peds_considered])
seq_list_rel.append(curr_seq_rel[:num_peds_considered])
self.num_seq = len(seq_list)
seq_list = np.concatenate(seq_list, axis=0)
seq_list_rel = np.concatenate(seq_list_rel, axis=0)
loss_mask_list = np.concatenate(loss_mask_list, axis=0)
non_linear_ped = np.asarray(non_linear_ped)
# Convert numpy -> Torch Tensor
self.obs_traj = torch.from_numpy(
seq_list[:, :, :self.obs_len]).type(torch.double)
self.pred_traj = torch.from_numpy(
seq_list[:, :, self.obs_len:]).type(torch.double)
self.obs_traj_rel = torch.from_numpy(
seq_list_rel[:, :, :self.obs_len]).type(torch.double)
self.pred_traj_rel = torch.from_numpy(
seq_list_rel[:, :, self.obs_len:]).type(torch.double)
self.loss_mask = torch.from_numpy(loss_mask_list).type(
torch.double)
self.non_linear_ped = torch.from_numpy(non_linear_ped).type(
torch.double)
cum_start_idx = [0] + np.cumsum(num_peds_in_seq).tolist()
self.seq_start_end = [
(start, end)
for start, end in zip(cum_start_idx, cum_start_idx[1:])
]
# Convert to Graphs
self.v_obs = []
self.A_obs = []
self.v_pred = []
self.A_pred = []
print("Processing Data .....")
pbar = tqdm(total=len(self.seq_start_end))
for ss in range(len(self.seq_start_end)):
pbar.update(1)
start, end = self.seq_start_end[ss]
v_, a_ = seq_to_graph(self.obs_traj[start:end, :],
self.obs_traj_rel[start:end, :],
self.norm_lap_matr)
self.v_obs.append(v_.clone())
self.A_obs.append(a_.clone())
v_, a_ = seq_to_graph(self.pred_traj[start:end, :],
self.pred_traj_rel[start:end, :],
self.norm_lap_matr)
self.v_pred.append(v_.clone())
self.A_pred.append(a_.clone())
pbar.close()
__data = {}
__data["obs_traj"] = self.obs_traj
__data["pred_traj"] = self.pred_traj
__data["obs_traj_rel"] = self.obs_traj_rel
__data["pred_traj_rel"] = self.pred_traj_rel
__data["non_linear_ped"] = self.non_linear_ped
__data["loss_mask"] = self.loss_mask
__data["v_obs"] = self.v_obs
__data["A_obs"] = self.A_obs
__data["v_pred"] = self.v_pred
__data["A_pred"] = self.A_pred
__data["num_seq"] = self.num_seq
__data["seq_start_end"] = self.seq_start_end
print("Saving dataset to:", pkl_path)
with open(pkl_path, "wb") as output_file:
pickle.dump(__data, output_file)
def __len__(self):
return self.num_seq
def __getitem__(self, index):
start, end = self.seq_start_end[index]
out = [
self.obs_traj[start:end, :], self.pred_traj[start:end, :],
self.obs_traj_rel[start:end, :], self.pred_traj_rel[start:end, :],
self.non_linear_ped[start:end], self.loss_mask[start:end, :],
self.v_obs[index], self.A_obs[index], self.v_pred[index],
self.A_pred[index], self.seq_start_end[index]
]
return out