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dataset.py
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import os
from collections import Counter
import joblib
import numpy as np
import torch
from torch.utils.data.dataset import Dataset
import IPython
def data_time_split(data_list,params):
input_time_step = params['input_time_step']
output_time_step = params['output_time_step']
trajs = data_list['traj']
speeds = data_list['speed']
features = data_list['feature']
actions = data_list['action']
x_traj,y_traj,x_traj_len=[],[],[]
y_intent = []
x_speed, y_speed = [],[]
x_feature, y_feature = [],[]
data_ids = []
inds=np.arange(0,len(trajs))
for ind in inds:
traj = trajs[ind]
speed = speeds[ind]
feature = features[ind]
action = actions[ind]
begin=0
end=input_time_step+output_time_step
steps=len(traj)
src_len = steps - output_time_step
mask_now=False
if src_len < input_time_step/4:
continue
if steps< end:
mask_now = True
pad_traj = np.array([traj[0]*0]*(end-steps))
traj = np.concatenate([pad_traj,traj])
pad_speed = np.array([speed[0] * 0] * (end - steps))
speed = np.concatenate([pad_speed, speed])
pad_feature= np.array([feature[0] * 0] * (end - steps))
feature = np.concatenate([pad_feature, speed])
pad_actions= np.array([action[0] * 0] * (end - steps))
action = np.concatenate([pad_actions, action])
steps = len(traj)
while end<=steps:
# input
inp_traj = traj[begin:begin+input_time_step].reshape((input_time_step, -1))
x_traj.append(inp_traj)
data_ids.append(ind)
if mask_now:
x_traj_len.append(src_len)
else:
x_traj_len.append(len(inp_traj))
inp_sp= speed[begin:begin+input_time_step].reshape((input_time_step, -1))
x_speed.append(inp_sp)
inp_feat= feature[begin:begin+input_time_step].reshape((input_time_step, -1))
x_feature.append(inp_feat)
# output
out_traj = traj[begin+input_time_step:end].reshape((output_time_step, -1))
y_traj.append(out_traj)
out_sp= speed[begin+input_time_step:end].reshape((output_time_step, -1))
y_speed.append(out_sp)
out_feat= feature[begin+input_time_step:end].reshape((output_time_step, -1))
y_feature.append(out_feat)
y_intent.append(action[begin+input_time_step-1])
begin += 1
end += 1
x_traj=np.array(x_traj)
x_speed = np.array(x_speed)
x_feature = np.array(x_feature)
y_traj=np.array(y_traj)
y_speed = np.array(y_speed)
y_feature = np.array(y_feature)
y_intent=np.array(y_intent)
x_traj_len = np.array(x_traj_len)
data_ids=np.array(data_ids) # for each window, describes the rollout number that it came from
pred_start_pos = x_traj[:,-1]
data ={'x_traj':x_traj,'x_speed':x_speed,'x_feature':x_feature,
'y_traj':y_traj,'y_speed':y_speed,'y_feature':y_feature,
'y_intent':y_intent,'pred_start_pos':pred_start_pos,
'x_traj_len':x_traj_len,'data_ids':data_ids}
return data
def normalize_data(data, data_stats):
new_data={}
for k,v in data.items():
if k in ['x_traj','x_speed','y_traj','y_speed','x_feature','y_feature']:
mark = k.split('_')[-1]
data_mean,data_std=data_stats[mark+'_mean'],data_stats[mark+'_std']
new_data[k] = (v-data_mean)/data_std
else:
new_data[k] = v
return new_data
class Trajectory_Data(Dataset):
def __init__(self, params, mode='train',data_stats={}):
self.mode = mode
print(mode,'data preprocessing')
cache_dir = params['log_dir']+mode+'.cache'
if os.path.exists(cache_dir):
print('loading data from cache',cache_dir)
self.data = joblib.load(cache_dir)
else:
raw_data = joblib.load(params['data_path'])[mode]
self.data = data_time_split(raw_data,params) # This just does windowing
if mode=='train':
data_stats['traj_mean'] = np.mean(self.data['x_traj'],axis=(0,1))
data_stats['traj_std'] = np.std(self.data['x_traj'], axis=(0, 1))
data_stats['speed_mean'] = np.mean(self.data['x_speed'],axis=(0,1))
data_stats['speed_std'] = np.std(self.data['x_speed'], axis=(0, 1))
data_stats['feature_mean'] = np.mean(self.data['x_feature'],axis=(0,1))
data_stats['feature_std'] = np.std(self.data['x_feature'], axis=(0, 1))
self.data['data_stats'] = data_stats
if params['normalize_data']:
if mode=='train':
print('data statistics:')
print(data_stats)
self.data = normalize_data(self.data, data_stats)
joblib.dump(self.data,cache_dir)
enc_inp= None
for feat in params['inp_feat']:
dat = self.data['x_'+feat]
if enc_inp is None:
enc_inp = dat
else:
enc_inp = np.concatenate([enc_inp,dat],axis=-1)
self.data['x_encoder'] = enc_inp
self.data['y_decoder'] = self.data['y_speed']
self.data['start_decode'] = self.data['x_speed'][:,-1]
self.input_time_step = params['input_time_step']
self.input_feat_dim = self.data['x_encoder'].shape[2]
print(mode + '_data size:', len(self.data['x_encoder']))
print('each category counts:')
print(Counter(self.data['y_intent']))
# print("In dataset.py")
# IPython.embed()
def __getitem__(self, index):
x = self.data['x_encoder'][index]
y_traj = self.data['y_decoder'][index]
y_inten = self.data['y_intent'][index]
start_decode = self.data['start_decode'][index] # this is for incremental decoding
pred_start_pos = self.data['pred_start_pos'][index]
x_len = self.data['x_traj_len'][index]
x_mask = np.zeros(shape=x.shape[0],dtype=np.int)
bias = self.input_time_step - x_len
# left pad
if bias>0:
x_mask[:bias] = 1
x[:bias] = 0
return (x, y_traj, y_inten, start_decode, pred_start_pos, x_mask)
def __len__(self):
return len(self.data['x_encoder'])
def get_data_loader(params, mode='train',pin_memory=False):
if mode == 'train':
train_data = Trajectory_Data(params, mode='train')
data_stats = train_data.data['data_stats']
train_loader = torch.utils.data.DataLoader(
train_data, batch_size=params['batch_size'], shuffle=True,
drop_last=True, pin_memory=pin_memory)
valid_data = Trajectory_Data(params, mode='valid',data_stats=data_stats)
valid_loader = torch.utils.data.DataLoader(
valid_data, batch_size=params['batch_size'], shuffle=False,
drop_last=False, pin_memory=pin_memory)
test_data = Trajectory_Data(params, mode='test',data_stats=data_stats)
test_loader = torch.utils.data.DataLoader(
test_data, batch_size=params['batch_size'], shuffle=False,
drop_last=False, pin_memory=pin_memory)
for k, v in data_stats.items():
data_stats[k] = [float(x) for x in v]
params['data_stats'] = data_stats
params['print_step'] = max(1,len(train_loader) // 10)
return train_loader, valid_loader, test_loader, params
elif mode == 'test':
data_stats = params['data_stats']
for k,v in data_stats.items():
data_stats[k] = np.array(v)
test_data = Trajectory_Data(params, mode='test',data_stats=data_stats)
test_loader = torch.utils.data.DataLoader(
test_data, batch_size=params['batch_size'], shuffle=False,
num_workers=1,drop_last=False, pin_memory=pin_memory)
return test_loader
elif mode == 'valid':
data_stats = params['data_stats']
for k,v in data_stats.items():
data_stats[k] = np.array(v)
test_data = Trajectory_Data(params, mode='valid',data_stats=data_stats)
test_loader = torch.utils.data.DataLoader(
test_data, batch_size=params['batch_size'], shuffle=False,
num_workers=1,drop_last=False, pin_memory=pin_memory)
return test_loader
else:
return None