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train.py
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import time
import os
import argparse
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
# import torchvision
# import torchvision.transforms as transforms
import numpy as np
import pandas as pd
from sklearn.metrics import recall_score, accuracy_score, average_precision_score, precision_score
import datasets
import network
import utils
from utils import data_loader
# Arguments
def parse_args():
parser = argparse.ArgumentParser(description='Train PV-LSTM network')
parser.add_argument('--data_dir', type=str,
help='Path to dataset',
required=True)
parser.add_argument('--dataset', type=str,
help='Datasets supported: jaad, jta, nuscenes',
required=True)
parser.add_argument('--out_dir', type=str,
help='Path to save output',
required=True)
parser.add_argument('--task', type=str,
help='Task the network is performing, choose between 2D_bounding_box-intention, \
3D_bounding_box, 3D_bounding_box-attribute',
required=True)
# data configuration
parser.add_argument('--input', type=int,
help='Input sequence length in frames',
required=True)
parser.add_argument('--output', type=int,
help='Output sequence length in frames',
required=True)
parser.add_argument('--stride', type=int,
help='Input and output sequence stride in frames',
required=True)
parser.add_argument('--skip', type=int, default=1)
parser.add_argument('--is_3D', type=bool, default=False)
# data loading / saving
parser.add_argument('--dtype', type=str, default='train')
parser.add_argument("--from_file", type=bool, default=False)
parser.add_argument('--save', type=bool, default=True)
parser.add_argument('--log_name', type=str, default='')
parser.add_argument('--loader_workers', type=int, default=10)
parser.add_argument('--loader_shuffle', type=bool, default=True)
parser.add_argument('--pin_memory', type=bool, default=False)
# training
parser.add_argument('--device', type=str, default='cuda')
parser.add_argument('--batch_size', type=int, default=100)
parser.add_argument('--n_epochs', type=int, default=100)
parser.add_argument('--lr', type=int, default=1e-5)
parser.add_argument('--lr_scheduler', type=bool, default=False)
# network
parser.add_argument('--hidden_size', type=int, default=512)
parser.add_argument('--hardtanh_limit', type=int, default=100)
args = parser.parse_args()
return args
# For 2D datasets
def train_2d(args, net, train, val):
print('='*100)
print('Training ...')
print('Task: ' + str(args.task))
print('Learning rate: ' + str(args.lr))
print('Number of epochs: ' + str(args.n_epochs))
print('Hidden layer size: ' + str(args.hidden_size) + '\n')
optimizer = optim.Adam(net.parameters(), lr=args.lr)
if args.lr_scheduler:
scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer, factor=0.5, patience=15,
threshold = 1e-8, verbose=True)
# init values
mse = nn.MSELoss()
bce = nn.BCELoss()
data = []
for epoch in range(args.n_epochs):
start = time.time()
avg_epoch_train_s_loss = 0
avg_epoch_val_s_loss = 0
avg_epoch_train_c_loss = 0
avg_epoch_val_c_loss = 0
ade = 0
fde = 0
aiou = 0
fiou = 0
avg_acc = 0
avg_rec = 0
avg_pre = 0
mAP = 0
counter = 0
for idx, (obs_s, target_s, obs_p, target_p, target_c, label_c) in enumerate(train):
counter += 1
obs_s = obs_s.to(device='cuda')
target_s = target_s.to(device='cuda')
obs_p = obs_p.to(device='cuda')
target_p = target_p.to(device='cuda')
target_c = target_c.to(device='cuda')
net.zero_grad()
speed_preds, crossing_preds = net(speed=obs_s, pos=obs_p)
speed_loss = mse(speed_preds, target_s)/100
crossing_loss = 0
for i in range(target_c.shape[1]):
crossing_loss += bce(crossing_preds[:,i], target_c[:,i])
crossing_loss /= target_c.shape[1]
loss = speed_loss + crossing_loss
loss.backward()
optimizer.step()
avg_epoch_train_s_loss += float(speed_loss)
avg_epoch_train_c_loss += float(crossing_loss)
avg_epoch_train_s_loss /= counter
avg_epoch_train_c_loss /= counter
counter=0
state_preds = []
state_targets = []
intent_preds = []
intent_targets = []
for idx, (obs_s, target_s, obs_p, target_p, target_c, label_c) in enumerate(val):
counter+=1
obs_s = obs_s.to(device='cuda')
target_s = target_s.to(device='cuda')
obs_p = obs_p.to(device='cuda')
target_p = target_p.to(device='cuda')
target_c = target_c.to(device='cuda')
with torch.no_grad():
speed_preds, crossing_preds, intentions = net(speed=obs_s, pos=obs_p, average=True)
speed_loss = mse(speed_preds, target_s)/100
crossing_loss = 0
for i in range(target_c.shape[1]):
crossing_loss += bce(crossing_preds[:,i], target_c[:,i])
crossing_loss /= target_c.shape[1]
avg_epoch_val_s_loss += float(speed_loss)
avg_epoch_val_c_loss += float(crossing_loss)
preds_p = utils.speed2pos(speed_preds, obs_p)
ade += float(utils.ADE(preds_p, target_p))
fde += float(utils.FDE(preds_p, target_p))
aiou += float(utils.AIOU(preds_p, target_p))
fiou += float(utils.FIOU(preds_p, target_p))
target_c = target_c[:,:,1].view(-1).cpu().numpy()
crossing_preds = np.argmax(crossing_preds.view(-1,2).detach().cpu().numpy(), axis=1)
label_c = label_c.view(-1).cpu().numpy()
intentions = intentions.view(-1).detach().cpu().numpy()
state_preds.extend(crossing_preds)
state_targets.extend(target_c)
intent_preds.extend(intentions)
intent_targets.extend(label_c)
avg_epoch_val_s_loss /= counter
avg_epoch_val_c_loss /= counter
ade /= counter
fde /= counter
aiou /= counter
fiou /= counter
avg_acc = accuracy_score(state_targets, state_preds)
avg_rec = recall_score(state_targets, state_preds, average='binary', zero_division=1)
avg_pre = precision_score(state_targets, state_preds, average='binary', zero_division=1)
mAP = average_precision_score(state_targets, state_preds, average=None)
intent_acc = accuracy_score(intent_targets, intent_preds)
intent_mAP = average_precision_score(intent_targets, intent_preds, average=None)
data.append([epoch, avg_epoch_train_s_loss, avg_epoch_val_s_loss, \
avg_epoch_train_c_loss, avg_epoch_val_c_loss, \
ade, fde, aiou, fiou, intent_acc])
if args.lr_scheduler:
scheduler.step(crossing_loss)
print('e:', epoch, '| ts: %.4f'% avg_epoch_train_s_loss, '| tc: %.4f'% avg_epoch_train_c_loss,
'| vs: %.4f'% avg_epoch_val_s_loss, '| vc: %.4f'% avg_epoch_val_c_loss, '| ade: %.4f'% ade,
'| fde: %.4f'% fde, '| aiou: %.4f'% aiou, '| fiou: %.4f'% fiou, '| state_acc: %.4f'% avg_acc,
'| intention_acc: %.4f'% intent_acc,
'| t:%.4f'%(time.time()-start))
df = pd.DataFrame(data, columns =['epoch', 'train_loss_s', 'val_loss_s', 'train_loss_c', 'val_loss_c',\
'ade', 'fde', 'aiou', 'fiou', 'intention_acc'])
if args.save:
print('\nSaving ...')
file = '{}_{}'.format(str(args.lr), str(args.hidden_size))
if args.lr_scheduler:
filename = 'data_' + file + '_scheduler.csv'
modelname = 'model_' + file + '_scheduler.pkl'
else:
filename = 'data_' + file + '.csv'
modelname = 'model_' + file + '.pkl'
df.to_csv(os.path.join(args.out_dir, args.log_name, filename), index=False)
torch.save(net.state_dict(), os.path.join(args.out_dir, args.log_name, modelname))
print('Training data and model saved to {}\n'.format(os.path.join(args.out_dir, args.log_name)))
print('='*100)
print('Done !')
# For 3D datasets
def train_3d(args, net, train, val):
print('='*100)
print('Training ...')
print('Task: ' + str(args.task))
print('Learning rate: ' + str(args.lr))
print('Number of epochs: ' + str(args.n_epochs))
print('Hidden layer size: ' + str(args.hidden_size) + '\n')
optimizer = optim.Adam(net.parameters(), lr=args.lr)
if args.lr_scheduler:
scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer, factor=0.5, patience=15,
threshold = 1e-8, verbose=True)
# init values
mse = nn.MSELoss()
bce = nn.BCELoss()
data = []
start = time.time()
avg_epoch_train_s_loss = 0
avg_epoch_val_s_loss = 0
ade = 0
fde = 0
aiou = 0
fiou = 0
if 'attribute' in args.task:
for epoch in range(args.n_epochs):
avg_epoch_train_a_loss = 0
avg_epoch_val_a_loss = 0
# TRAINING
counter = 0
for idx, values in enumerate(train):
print(len(values))
(obs_s, target_s, obs_p, target_p, target_a) = values
counter += 1
obs_s = obs_s.to(device='cuda')
target_s = target_s.to(device='cuda')
obs_p = obs_p.to(device='cuda')
target_p = target_p.to(device='cuda')
target_a = target_a.to(device='cuda')
net.zero_grad()
speed_preds, attrib_preds = net(speed=obs_s, pos=obs_p) #[100,16,6]
speed_loss = mse(speed_preds, target_s)
attrib_loss = mse(attrib_preds, target_a)
attrib_loss = 0
for i in range(target_a.shape[1]):
attrib_loss += bce(attrib_preds[:,i], target_a[:,i])
attrib_loss /= target_a.shape[1]
loss = speed_loss + attrib_loss
loss.backward()
optimizer.step()
avg_epoch_train_s_loss += float(speed_loss)
avg_epoch_train_a_loss += float(attrib_loss)
avg_epoch_train_s_loss /= counter
avg_epoch_train_a_loss /= counter
# VALIDATION
counter=0
for idx, (obs_s, target_s, obs_p, target_p, target_a) in enumerate(val):
counter += 1
obs_s = obs_s.to(device='cuda')
target_s = target_s.to(device='cuda')
obs_p = obs_p.to(device='cuda')
target_p = target_p.to(device='cuda')
target_a = target_a.to(device='cuda')
with torch.no_grad():
speed_preds, attrib_preds = net(speed=obs_s, pos=obs_p) #[100,16,6]
speed_loss = mse(speed_preds, target_s)
# attrib_loss = mse(attrib_preds, target_a)
attrib_loss = 0
for i in range(target_a.shape[1]):
attrib_loss += bce(attrib_preds[:,i], target_a[:,i])
attrib_loss /= target_a.shape[1]
avg_epoch_val_s_loss += float(speed_loss)
avg_epoch_val_a_loss += float(attrib_loss)
preds_p = utils.speed2pos(speed_preds, obs_p, is_3D=True)
ade += float(utils.ADE(preds_p, target_p, is_3D=True))
fde += float(utils.FDE(preds_p, target_p, is_3D=True))
aiou += float(utils.AIOU(preds_p, target_p, is_3D=True))
fiou += float(utils.FIOU(preds_p, target_p, is_3D=True))
avg_epoch_val_s_loss /= counter
avg_epoch_val_a_loss /= counter
if args.lr_scheduler:
scheduler.step(attrib_loss)
ade /= counter
fde /= counter
aiou /= counter
fiou /= counter
data.append([epoch, avg_epoch_train_s_loss, avg_epoch_val_s_loss, avg_epoch_train_a_loss, avg_epoch_val_a_loss,\
ade, fde, aiou, fiou])
print('e:', epoch, '| ts: %.6f'% avg_epoch_train_s_loss,
'| vs: %.6f'% avg_epoch_val_s_loss,
'| ta: %.6f'% avg_epoch_train_a_loss,
'| va: %.6f'% avg_epoch_val_a_loss,
'| ade: %.4f'% ade, '| fde: %.4f'% fde,
'| aiou: %.4f'% aiou, '| fiou: %.4f'% fiou,
'| t:%.4f'%(time.time()-start))
df = pd.DataFrame(data, columns =['epoch', 'train_loss', 'val_loss', 'train_attrib_loss', 'val_attrib_loss',\
'ade', 'fde', 'aiou', 'fiou'])
else:
for epoch in range(args.n_epochs):
# TRAINING
counter = 0
for idx, (obs_s, target_s, obs_p, target_p) in enumerate(train):
counter += 1
obs_s = obs_s.to(device='cuda')
target_s = target_s.to(device='cuda')
obs_p = obs_p.to(device='cuda')
target_p = target_p.to(device='cuda')
net.zero_grad()
if 'attribute' in args.task:
speed_preds, attrib_preds = net(speed=obs_s, pos=obs_p) #[100,16,6]
speed_loss = mse(speed_preds, target_s)
attrib_loss = mse(attrib_preds, target_a)
loss = speed_loss
loss.backward()
optimizer.step()
avg_epoch_train_s_loss += float(speed_loss)
else:
speed_preds = net(speed=obs_s, pos=obs_p)[0] #[100,16,6]
speed_loss = mse(speed_preds, target_s)
loss = speed_loss
loss.backward()
optimizer.step()
avg_epoch_train_s_loss += float(speed_loss)
avg_epoch_train_s_loss /= counter
# VALIDATION
counter=0
for idx, (obs_s, target_s, obs_p, target_p) in enumerate(val):
counter += 1
obs_s = obs_s.to(device='cuda')
target_s = target_s.to(device='cuda')
obs_p = obs_p.to(device='cuda')
target_p = target_p.to(device='cuda')
with torch.no_grad():
speed_preds = net(speed=obs_s, pos=obs_p)[0]
speed_loss = mse(speed_preds, target_s)
avg_epoch_val_s_loss += float(speed_loss)
preds_p = utils.speed2pos(speed_preds, obs_p, is_3D=True)
ade += float(utils.ADE(preds_p, target_p, is_3D=True))
fde += float(utils.FDE(preds_p, target_p, is_3D=True))
aiou += float(utils.AIOU(preds_p, target_p, is_3D=True))
fiou += float(utils.FIOU(preds_p, target_p, is_3D=True))
avg_epoch_val_s_loss /= counter
if args.lr_scheduler:
scheduler.step(avg_epoch_val_s_loss)
ade /= counter
fde /= counter
aiou /= counter
fiou /= counter
data.append([epoch, avg_epoch_train_s_loss, avg_epoch_val_s_loss, \
ade, fde, aiou, fiou])
print('e:', epoch, '| ts: %.6f'% avg_epoch_train_s_loss,
'| vs: %.6f'% avg_epoch_val_s_loss,
'| ade: %.4f'% ade, '| fde: %.4f'% fde,
'| aiou: %.4f'% aiou, '| fiou: %.4f'% fiou,
'| t:%.4f'%(time.time()-start))
df = pd.DataFrame(data, columns =['epoch', 'train_loss', 'val_loss', \
'ade', 'fde', 'aiou', 'fiou'])
if args.save:
print('\nSaving ...')
file = '{}_{}'.format(str(args.lr), str(args.hidden_size))
if args.lr_scheduler:
filename = 'data_' + file + '_scheduler.csv'
modelname = 'model_' + file + '_scheduler.pkl'
else:
filename = 'data_' + file + '.csv'
modelname = 'model_' + file + '.pkl'
df.to_csv(os.path.join(args.out_dir, args.log_name, filename), index=False)
torch.save(net.state_dict(), os.path.join(args.out_dir, args.log_name, modelname))
print('Training data and model saved to {}\n'.format(os.path.join(args.out_dir, args.log_name)))
print('='*100)
print('Done !')
if __name__ == '__main__':
args = parse_args()
# create output dir
if not args.log_name:
args.log_name = '{}_{}_{}_{}'.format(args.dataset, str(args.input),\
str(args.output), str(args.stride))
if not os.path.isdir(os.path.join(args.out_dir, args.log_name)):
os.mkdir(os.path.join(args.out_dir, args.log_name))
# select dataset
if args.dataset == 'jaad':
args.is_3D = False
elif args.dataset == 'jta':
args.is_3D = True
elif args.dataset == 'nuscenes':
args.is_3D = True
else:
print('Unknown dataset entered! Please select from available datasets: jaad, jta, nuscenes...')
# load data
train_set = eval('datasets.' + args.dataset)(
data_dir=args.data_dir,
out_dir=os.path.join(args.out_dir, args.log_name),
dtype='train',
input=args.input,
output=args.output,
stride=args.stride,
skip=args.skip,
task=args.task,
from_file=args.from_file,
save=args.save
)
train_loader = data_loader(args, train_set)
val_set = eval('datasets.' + args.dataset)(
data_dir=args.data_dir,
out_dir=os.path.join(args.out_dir, args.log_name),
dtype='val',
input=args.input,
output=args.output,
stride=args.stride,
skip=args.skip,
task=args.task,
from_file=args.from_file,
save=args.save
)
val_loader = data_loader(args, val_set)
# initiate network
net = network.PV_LSTM(args).to(args.device)
# training
if not args.is_3D:
train_2d(args, net, train_loader, val_loader)
else:
train_3d(args, net, train_loader, val_loader)