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from glob import glob
from einops import rearrange
import math
import matplotlib.pyplot as plt
import torch.nn.functional as F
import numpy as np
import torch
from torch.utils.data import Dataset
from tqdm import tqdm
import pickle
from utils.normalization import normalize_min_max
from torch.nn.utils.rnn import pad_sequence
def rotate_traj(past_rel, future_rel, past_abs, rotate_time_frame=0):
"""
@params past_rel: [N, A, P, 2]
@params future_rel: [N, A, F, 2]
@params past_abs: [N, A, P, 2]
@params rotate_time_frame: int
"""
A = past_rel.size(1)
past_rel = rearrange(past_rel, 'b a p d -> (b a) p d')
past_abs = rearrange(past_abs, 'b a p d -> (b a) p d')
future_rel = rearrange(future_rel, 'b a f d -> (b a) f d')
past_diff = past_rel[:, rotate_time_frame]
# past_diff = past[:, rotate_time_frame] - past[:, rotate_time_frame-1]
past_theta = torch.atan(torch.div(past_diff[:, 1], past_diff[:, 0] + 1e-5))
past_theta = torch.where((past_diff[:, 0] < 0), past_theta + math.pi, past_theta)
rotate_matrix = torch.zeros((past_theta.size(0), 2, 2)).to(past_theta.device)
rotate_matrix[:, 0, 0] = torch.cos(past_theta)
rotate_matrix[:, 0, 1] = torch.sin(past_theta)
rotate_matrix[:, 1, 0] = -torch.sin(past_theta)
rotate_matrix[:, 1, 1] = torch.cos(past_theta)
past_after = torch.matmul(rotate_matrix, past_rel.transpose(1, 2)).transpose(1, 2) # [N, P, 2]
future_after = torch.matmul(rotate_matrix, future_rel.transpose(1, 2)).transpose(1, 2) # [N, F, 2]
past_abs_after = torch.matmul(rotate_matrix, past_abs.transpose(1, 2)).transpose(1, 2) # [N, P, 2]
past_after = rearrange(past_after, '(b a) p d -> b a p d', a=A)
future_after = rearrange(future_after, '(b a) f d -> b a f d', a=A)
past_abs_after = rearrange(past_abs_after, '(b a) p d -> b a p d', a=A)
return past_after, future_after, past_abs_after
def seq_collate_sdd(batch):
(index, past_traj, fut_traj, past_traj_orig, fut_traj_orig, traj_vel) = zip(*batch)
indexes = torch.stack(index, dim=0)
pre_motion_3D = torch.stack(past_traj,dim=0)
fut_motion_3D = torch.stack(fut_traj,dim=0)
pre_motion_3D_orig = torch.stack(past_traj_orig, dim=0)
fut_motion_3D_orig = torch.stack(fut_traj_orig, dim=0)
fut_traj_vel = torch.stack(traj_vel, dim=0)
batch_size = torch.tensor(pre_motion_3D.shape[0]) ### bt
data = {
'indexes': indexes,
'batch_size': batch_size,
'past_traj': pre_motion_3D,
'fut_traj': fut_motion_3D,
'past_traj_original_scale': pre_motion_3D_orig,
'fut_traj_original_scale': fut_motion_3D_orig,
'fut_traj_vel': fut_traj_vel,
}
return data
def seq_collate_imle_train(batch):
(past_traj, fut_traj, past_traj_orig, fut_traj_orig, traj_vel, y_t, y_pred_data) = zip(*batch)
pre_motion_3D = torch.stack(past_traj,dim=0)
fut_motion_3D = torch.stack(fut_traj,dim=0)
pre_motion_3D_orig = torch.stack(past_traj_orig, dim=0)
fut_motion_3D_orig = torch.stack(fut_traj_orig, dim=0)
fut_traj_vel = torch.stack(traj_vel, dim=0)
y_t = torch.stack(y_t, dim=0)
y_pred_data = torch.stack(y_pred_data,dim=0)
batch_size = torch.tensor(pre_motion_3D.shape[0]) ### bt
data = {
'batch_size': batch_size,
'past_traj': pre_motion_3D,
'fut_traj': fut_motion_3D,
'past_traj_original_scale': pre_motion_3D_orig,
'fut_traj_original_scale': fut_motion_3D_orig,
'fut_traj_vel': fut_traj_vel,
'y_t': y_t,
'y_pred_data': y_pred_data
}
return data
class SDDDataset(Dataset):
def __init__(self, cfg, data_dir,
training=True, overfit=False, rotate_time_frame=0, imle=False):
super(SDDDataset, self).__init__()
"""init"""
self.cfg = cfg
dataset_file = os.path.join(data_dir, 'original/sdd_train.pkl') if training else os.path.join(data_dir, 'original/sdd_test.pkl')
if overfit:
dataset_file = os.path.join(data_dir, 'original/sdd_train.pkl')
### Compare with NSP model
# dataset_file = os.path.join(data_dir, 'nsp/sdd_nsp_train.pkl') if training else os.path.join(data_dir, 'nsp/sdd_nsp_test.pkl')
# if overfit:
# dataset_file = os.path.join(data_dir, 'nsp/sdd_nsp_train.pkl')
### Compare with NSP model
self.training = training
self.overfit = overfit
self.rotate_time_frame = rotate_time_frame
self.imle = imle
self.past_frames = cfg.past_frames
self.future_frames = cfg.future_frames
self.seq_len = self.past_frames + self.future_frames
self.max_agents_per_scene = 0
assert self.seq_len == 20 and self.past_frames == 8, "Sanity check on frame length failed!"
"""load the data in the original scale"""
all_data = pickle.load(open(dataset_file, 'rb'))
print("Mode: {:s}, {:d} sequences".format('train' if training else 'test', len(all_data)))
"""process the data"""
### set the agent_num in the cfg
cfg.MODEL.CONTEXT_ENCODER.AGENTS = cfg.agents
### compute past and future trajectories
past_traj_abs = torch.from_numpy(np.stack([scene[0] for scene in all_data], axis=0)).unsqueeze(1) # [N, 1, T, 2]
### Compare with NSP model
# past_traj_abs = torch.from_numpy(all_data[:,:self.past_frames])[...,:2].unsqueeze(1).float() # [N, 1, T, 2]
### Compare with NSP model
initial_pos = past_traj_abs[:, :, -1:, :] # [N, 1, 1, 2]
past_traj_rel = (past_traj_abs - initial_pos).contiguous() # [N, 1, T, 2]
fut_traj_abs = torch.from_numpy(np.stack([scene[1] for scene in all_data], axis=0)).unsqueeze(1) # [N, 1, T, 2]
### Compare with NSP model
# fut_traj_abs = torch.from_numpy(all_data[:,self.past_frames:])[...,:2].unsqueeze(1).float() # [N, 1, T, 2]
### Compare with NSP model
fut_traj_rel = (fut_traj_abs - initial_pos).contiguous() # [N, 1, T, 2]
if cfg.rotate:
past_traj_rel, fut_traj_rel, past_traj_abs = rotate_traj(past_traj_rel, fut_traj_rel, past_traj_abs, rotate_time_frame)
past_traj_vel = torch.cat((past_traj_rel[:, :, 1:] - past_traj_rel[:, :, :-1], torch.zeros_like(past_traj_rel[:,:, -1:])), dim=2)
past_traj = torch.cat((past_traj_abs, past_traj_rel, past_traj_vel), dim=-1)
self.fut_traj_vel = torch.cat((fut_traj_rel[:, :, 1:] - fut_traj_rel[:,:, :-1], torch.zeros_like(fut_traj_rel[:, :, -1:])), dim=2)
self.rotate_aug = cfg.rotate_aug and training
if training:
cfg.fut_traj_max = fut_traj_rel.max()
cfg.fut_traj_min = fut_traj_rel.min()
cfg.past_traj_max = past_traj.max()
cfg.past_traj_min = past_traj.min()
### record the original to avoid numerical errors
self.past_traj_original_scale = past_traj
self.fut_traj_original_scale = fut_traj_rel
### min-max normalization to make past_traj in [-1, 1]
self.past_traj = normalize_min_max(past_traj, cfg.past_traj_min, cfg.past_traj_max, -1, 1).contiguous()
### min-max normalization to make fut_traj in [-1, 1]
self.fut_traj = normalize_min_max(fut_traj_rel, cfg.fut_traj_min, cfg.fut_traj_max, -1, 1).contiguous()
"""load distillation target"""
if imle:
os.makedirs(os.path.join(data_dir, 'imle'), exist_ok=True)
pkl_ls = sorted(glob(os.path.join(data_dir, f'imle/*train*.pkl')))
keys_ls = ['past_traj', 'fut_traj', 'past_traj_original_scale', 'fut_traj_original_scale', 'fut_traj_vel', 'y_t', 'y_pred_data']
imle_data_dict = {}
total_scenes_loaded_ = 0
for i_pkl, cur_pkl in enumerate(pkl_ls):
data = pickle.load(open(cur_pkl, 'rb'))
if i_pkl == 0:
self.imle_meta_data = data['meta_data']
for key in keys_ls:
if key not in imle_data_dict:
imle_data_dict[key] = []
if key == 'y_t':
imle_data_dict[key].append(data[key][:, -1])
else:
imle_data_dict[key].append(data[key])
total_scenes_loaded_ += data['past_traj'].shape[0]
if total_scenes_loaded_ >= len(self.past_traj):
break
if i_pkl == 0:
# y_t_original_scale_ = unnormalize_min_max(torch.from_numpy(data['y_t'][:, -1]), cfg.fut_traj_min, cfg.fut_traj_max, -1, 1)
# y_pred_data_original_scale_ = torch.from_numpy(data['y_pred_data'])
# assert torch.sum(torch.abs(y_t_original_scale_ - y_pred_data_original_scale_)) < 1e-5, 'IMLE data is not consistent'
past_tarj_original_scale_ = torch.from_numpy(data['past_traj_original_scale'])
assert torch.sum(torch.abs(past_tarj_original_scale_[:10] - self.past_traj_original_scale[:10])) < 1e-5, 'IMLE data is not consistent'
pass
# concat the data
for key in keys_ls:
imle_data_dict[key] = torch.from_numpy(np.concatenate(imle_data_dict[key], axis=0))[:len(self.past_traj)]
self.imle_data_dict = imle_data_dict
def __len__(self):
return len(self.past_traj)
def __getitem__(self, item):
if self.imle:
out = [
self.imle_data_dict['past_traj'][item],
self.imle_data_dict['fut_traj'][item],
self.imle_data_dict['past_traj_original_scale'][item],
self.imle_data_dict['fut_traj_original_scale'][item],
self.imle_data_dict['fut_traj_vel'][item],
self.imle_data_dict['y_t'][item],
self.imle_data_dict['y_pred_data'][item]
]
else:
### past traj, future traj, number of pedestrians (presumbly?), index
past_traj_norm_scale = self.past_traj[item] # [A, P, 6]
fut_traj_norm_scale = self.fut_traj[item] # [A, F, 2]
past_traj_original_scale = self.past_traj_original_scale[item] # [A, P, 6]
fut_traj_original_scale = self.fut_traj_original_scale[item] # [A, F, 2]
fut_traj_vel = self.fut_traj_vel[item] # [A, F, 2]
out = [
torch.Tensor([item]).to(torch.int32),
past_traj_norm_scale,
fut_traj_norm_scale,
past_traj_original_scale,
fut_traj_original_scale,
fut_traj_vel,
]
return out
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