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trajectories.py
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trajectories.py
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import logging
import math
import numpy as np
import torch
from torch.utils.data import Dataset
logger = logging.getLogger(__name__)
class TrajectoryDataset(Dataset):
"""
Dataloader for the ETH-UCY Trajectory Dataset
"""
def __init__(
self,
data_dir,
alpha_e,
obs_len=8,
fut_len=12,
skip=1,
min_ped=1,
delim="\t",
n_coordinates=2,
add_confidence=True,
):
"""
Args:
- data_dir: Directory containing dataset file in the format
<frame_id> <ped_id> <x> <y>
- alpha_e: domain shift
- obs_len: Number of time-steps in input trajectories
- fut_len: Number of time-steps in output trajectories
- skip: Number of frames to skip while making the dataset
- min_ped: Minimum number of pedestrians that should be in a seqeunce
- delim: Delimiter in the dataset files
- n_coordinates: number of (input) coordinates (i.e. x, y)
- add_confidence: add an artificial spurious feature representing
the confidence of feature tracking
"""
super(TrajectoryDataset, self).__init__()
self.data_dir = data_dir
self.obs_len = obs_len
self.fut_len = fut_len
self.skip = skip
self.seq_len = self.obs_len + self.fut_len
self.delim = delim
self.alpha_e = alpha_e
self.n_coordinates = n_coordinates
self.add_confidence = add_confidence
# initialization
num_peds_in_seq = []
seq_list = []
seq_list_rel = []
data = read_file(self.data_dir, delim) # 2d array: _ X (frame, ped, coord)
# allocate dimension for syntethic variable
if self.add_confidence:
data = np.pad(data, ((0,0),(0,1)), mode='constant', constant_values=0)
frames = np.unique(data[:, 0]).tolist() # 1d array: unique frames
num_sequences = int(math.ceil((len(frames) - self.seq_len + 1) / skip)) # number of sequences
frame_data = []
for frame in frames:
frame_data.append(data[frame == data[:, 0], :]) # list of 2d array: [(_ X (frame, ped, coord)) per frame]
for idx in range(0, num_sequences * self.skip, skip): # for all the sequences
curr_seq_data = np.concatenate(
frame_data[idx : idx + self.seq_len], axis=0
) # 2d array: _ X (frame, ped, coord)
peds_in_curr_seq = np.unique(curr_seq_data[:, 1]) # 1d array: pedestrian (unique in seq)
curr_seq_rel = np.zeros((len(peds_in_curr_seq), self.n_coordinates+self.add_confidence, self.seq_len)) # null 3d array: ped X coord X frame
curr_seq = np.zeros((len(peds_in_curr_seq), self.n_coordinates+self.add_confidence, self.seq_len)) # null 3d array: ped X coord X frame
num_peds_considered = 0
for ped_id in peds_in_curr_seq: # for all pedestrians in seq
curr_ped_seq = curr_seq_data[curr_seq_data[:, 1] == ped_id, :] # 2d array: _ X (frame, ped, coord)
pad_front = frames.index(curr_ped_seq[0, 0]) - idx # idx of the first Ped'soccurence in the sequence
pad_end = frames.index(curr_ped_seq[-1, 0]) - idx + 1 # idx of the last Ped'soccurence in the sequence
# ignore pedestrians partially framed
if pad_end - pad_front != self.seq_len:
continue
# relative coordinates
curr_ped_seq = np.transpose(curr_ped_seq[:, 2:]) # 2d array: coord X frame
rel_curr_ped_seq = np.zeros(curr_ped_seq.shape) # null 2d array: coord X frame
rel_curr_ped_seq[:, 1:] = curr_ped_seq[:, 1:] - curr_ped_seq[:, :-1] # 2d array: coord X frame
# add synthetic spurious variable
if self.add_confidence:
rel_curr_ped_seq[self.n_coordinates,:self.obs_len] = np.linalg.norm(
rel_curr_ped_seq[:,self.obs_len:2*self.obs_len]-rel_curr_ped_seq[:,:self.obs_len],
ord=2,
axis=0
) # 2d array: (x,y, curv) X frame
rel_curr_ped_seq[self.n_coordinates,:self.obs_len] = self.alpha_e * (rel_curr_ped_seq[self.n_coordinates,:self.obs_len]+1) # linear
# 2d array: (x,y, conf) X frame
rel_curr_ped_seq = np.around(rel_curr_ped_seq, decimals=4)
curr_ped_seq = np.around(curr_ped_seq, decimals=4)
_idx = num_peds_considered
curr_seq[_idx, :, pad_front:pad_end] = curr_ped_seq # 3d array: ped X coord X frame
curr_seq_rel[_idx, :, pad_front:pad_end] = rel_curr_ped_seq # 3d array: ped X rel_coord X frame
num_peds_considered += 1
if num_peds_considered > min_ped: # filter only sequences with multi-agents
num_peds_in_seq.append(num_peds_considered) # list number of pedestrians per seq
seq_list.append(curr_seq[:num_peds_considered]) # list of 3d array: ped X coord X frame
seq_list_rel.append(curr_seq_rel[:num_peds_considered]) # list of 3d array: ped X rel_coord X frame
self.num_seq = len(seq_list)
seq_list = np.concatenate(seq_list, axis=0) # 3d array: (ped X coord X frame) seq by seq
seq_list_rel = np.concatenate(seq_list_rel, axis=0) # 3d array: (ped X rel_coord X frame) seq by seq
# Convert numpy -> Torch Tensor
self.obs_traj = torch.from_numpy(seq_list[:, :self.n_coordinates, : self.obs_len]).type(
torch.float
) # 3d tensor: (ped X coord X frame) seq by seq
self.fut_traj = torch.from_numpy(seq_list[:, :self.n_coordinates, self.obs_len :]).type(
torch.float
) # 3d tensor: (ped X coord X frame) seq by seq
self.obs_traj_rel = torch.from_numpy(seq_list_rel[:, :, : self.obs_len]).type(
torch.float
) # 3d tensor: (ped X rel_coord+conf X frame) seq by seq
self.fut_traj_rel = torch.from_numpy(seq_list_rel[:, :, self.obs_len :]).type(
torch.float
) # 3d tensor: (ped X rel_coord+conf X frame) seq by seq
cum_start_idx = [0] + np.cumsum(num_peds_in_seq).tolist() # list of cumulative number of peds per seq
self.seq_start_end = [
(start, end) for start, end in zip(cum_start_idx, cum_start_idx[1:])
] # list of couples for each sequence: n ped seen, n ped seen + n ped in current seq
def __len__(self):
return self.num_seq
def __getitem__(self, index): # index of the sequence
start, end = self.seq_start_end[index]
out = [
self.obs_traj[start:end, :], # 3d tensor: ped X coord X frame
self.fut_traj[start:end, :], # 3d tensor: ped X coord X frame
self.obs_traj_rel[start:end, :], # 3d tensor: ped X rel_coord X frame
self.fut_traj_rel[start:end, :], # 3d tensor: ped X rel_coord X frame
]
return out
class SynTrajectoryDataset(Dataset):
"""
Dataloader for the Synthetic Trajectory datasets
"""
def __init__(self,
data_dir='datasets/synthetic/train/orca_circle_crossing_4_ped_10000_scenes_0.2_radius_2.0_horizon.npz',
obs_len=8,
):
"""
Args:
- data_dir: Directory containing dataset file in the format
<seq_id> <ped_id> <x> <y>
- obs_len: : Number of time-steps in input trajectories
"""
super(SynTrajectoryDataset, self).__init__()
self.obs_len = obs_len
# load synthetic data (seq, ped, coord, frame)
data = torch.from_numpy(np.load(data_dir)['raw']).type(torch.float).permute(0,1,3,2)
# Split observed trajectory (features) and future trajectory (target)
self.obs_traj = data[:,:,:,:self.obs_len]
self.fut_traj = data[:,:,:,self.obs_len:]
self.obs_traj_rel = torch.zeros_like(self.obs_traj)
self.obs_traj_rel[:,:,:,1:] = self.obs_traj[:,:,:,1:]-self.obs_traj[:,:,:,:-1]
self.fut_traj_rel = torch.zeros_like(self.fut_traj)
self.fut_traj_rel[:,:,:,1:] = self.fut_traj[:,:,:,1:]-self.fut_traj[:,:,:,:-1]
def __len__(self):
return self.obs_traj.shape[0]
def __getitem__(self, index): # index of the sequence
out = [
self.obs_traj[index], # 3d tensor: ped X coord X frame
self.fut_traj[index], # 3d tensor: ped X coord X frame
self.obs_traj_rel[index], # 3d tensor: ped X rel_coord X frame
self.fut_traj_rel[index], # 3d tensor: ped X rel_coord X frame
]
return out
def seq_collate(data):
'''
Input:
Data format: batch of groups of pedestrians X coord X frame
Output:
LSTM input format: frame X batch of groups of pedestrians X coord
'''
(
obs_seq_list,
fut_seq_list,
obs_seq_rel_list,
fut_seq_rel_list,
) = zip(*data)
_len = [len(seq) for seq in obs_seq_list] # lists of n_ped
cum_start_idx = [0] + np.cumsum(_len).tolist()
seq_start_end = [
[start, end] for start, end in zip(cum_start_idx, cum_start_idx[1:])
]
obs_traj = torch.cat(obs_seq_list, dim=0).permute(2, 0, 1)
fut_traj = torch.cat(fut_seq_list, dim=0).permute(2, 0, 1)
obs_traj_rel = torch.cat(obs_seq_rel_list, dim=0).permute(2, 0, 1)
fut_traj_rel = torch.cat(fut_seq_rel_list, dim=0).permute(2, 0, 1)
seq_start_end = torch.LongTensor(seq_start_end)
out = [
obs_traj,
fut_traj,
obs_traj_rel,
fut_traj_rel,
seq_start_end,
]
return tuple(out)
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)