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train_base_learner.py
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train_base_learner.py
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from __future__ import print_function
import torch
from model import highwayNet
from utils import ngsimDataset,maskedNLL,maskedMSE,maskedNLLTest
from torch.utils.data import DataLoader
import time
import math
## Network Arguments
args = {}
args['use_cuda'] = True
args['encoder_size'] = 64
args['decoder_size'] = 128
args['in_length'] = 16
args['out_length'] = 25
args['grid_size'] = (13,3)
args['soc_conv_depth'] = 64
args['conv_3x1_depth'] = 16
args['dyn_embedding_size'] = 32
args['input_embedding_size'] = 32
args['num_lat_classes'] = 3
args['num_lon_classes'] = 2
args['use_maneuvers'] = True
args['train_flag'] = True
# Initialize network
net = highwayNet(args)
if args['use_cuda']:
net = net.cuda()
## Initialize optimizer
pretrainEpochs = 5
trainEpochs = 3
optimizer = torch.optim.Adam(net.parameters())
batch_size = 128
crossEnt = torch.nn.BCELoss()
## Initialize data loaders
trSet = ngsimDataset('data/TrainSet.mat')
valSet = ngsimDataset('data/ValSet.mat')
trDataloader = DataLoader(trSet,batch_size=batch_size,shuffle=True,num_workers=8,collate_fn=trSet.collate_fn)
valDataloader = DataLoader(valSet,batch_size=batch_size,shuffle=True,num_workers=8,collate_fn=valSet.collate_fn)
## Variables holding train and validation loss values:
train_loss = []
val_loss = []
prev_val_loss = math.inf
for epoch_num in range(pretrainEpochs+trainEpochs):
if epoch_num == 0:
print('Pre-training with MSE loss')
elif epoch_num == pretrainEpochs:
print('Training with NLL loss')
## Train:_________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________
net.train_flag = True
# Variables to track training performance:
avg_tr_loss = 0
avg_tr_time = 0
avg_lat_acc = 0
avg_lon_acc = 0
for i, data in enumerate(trDataloader):
st_time = time.time()
hist, nbrs, mask, lat_enc, lon_enc, fut, op_mask = data
if args['use_cuda']:
hist = hist.cuda()
nbrs = nbrs.cuda()
mask = mask.cuda()
lat_enc = lat_enc.cuda()
lon_enc = lon_enc.cuda()
fut = fut.cuda()
op_mask = op_mask.cuda()
# Forward pass
if args['use_maneuvers']:
fut_pred, lat_pred, lon_pred = net(hist, nbrs, mask, lat_enc, lon_enc)
# Pre-train with MSE loss to speed up training
if epoch_num < pretrainEpochs:
l = maskedMSE(fut_pred, fut, op_mask)
else:
# Train with NLL loss
l = maskedNLL(fut_pred, fut, op_mask) + crossEnt(lat_pred, lat_enc) + crossEnt(lon_pred, lon_enc)
avg_lat_acc += (torch.sum(torch.max(lat_pred.data, 1)[1] == torch.max(lat_enc.data, 1)[1])).item() / lat_enc.size()[0]
avg_lon_acc += (torch.sum(torch.max(lon_pred.data, 1)[1] == torch.max(lon_enc.data, 1)[1])).item() / lon_enc.size()[0]
else:
fut_pred = net(hist, nbrs, mask, lat_enc, lon_enc)
if epoch_num < pretrainEpochs:
l = maskedMSE(fut_pred, fut, op_mask)
else:
l = maskedNLL(fut_pred, fut, op_mask)
# Backprop and update weights
optimizer.zero_grad()
l.backward()
a = torch.nn.utils.clip_grad_norm_(net.parameters(), 10)
optimizer.step()
# Track average train loss and average train time:
batch_time = time.time()-st_time
avg_tr_loss += l.item()
avg_tr_time += batch_time
if i%100 == 99:
eta = avg_tr_time/100*(len(trSet)/batch_size-i)
print("Epoch no:",epoch_num+1,"| Epoch progress(%):",format(i/(len(trSet)/batch_size)*100,'0.2f'), "| Avg train loss:",format(avg_tr_loss/100,'0.4f'),"| Acc:",format(avg_lat_acc,'0.4f'),format(avg_lon_acc,'0.4f'), "| Validation loss prev epoch",format(prev_val_loss,'0.4f'), "| ETA(s):",int(eta))
train_loss.append(avg_tr_loss/100)
avg_tr_loss = 0
avg_lat_acc = 0
avg_lon_acc = 0
avg_tr_time = 0
# _________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________
## Validate:______________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________
net.train_flag = False
print("Epoch",epoch_num+1,'complete. Calculating validation loss...')
avg_val_loss = 0
avg_val_lat_acc = 0
avg_val_lon_acc = 0
val_batch_count = 0
total_points = 0
for i, data in enumerate(valDataloader):
st_time = time.time()
hist, nbrs, mask, lat_enc, lon_enc, fut, op_mask = data
if args['use_cuda']:
hist = hist.cuda()
nbrs = nbrs.cuda()
mask = mask.cuda()
lat_enc = lat_enc.cuda()
lon_enc = lon_enc.cuda()
fut = fut.cuda()
op_mask = op_mask.cuda()
# Forward pass
if args['use_maneuvers']:
if epoch_num < pretrainEpochs:
# During pre-training with MSE loss, validate with MSE for true maneuver class trajectory
net.train_flag = True
fut_pred, _ , _ = net(hist, nbrs, mask, lat_enc, lon_enc)
l = maskedMSE(fut_pred, fut, op_mask)
else:
# During training with NLL loss, validate with NLL over multi-modal distribution
fut_pred, lat_pred, lon_pred = net(hist, nbrs, mask, lat_enc, lon_enc)
l = maskedNLLTest(fut_pred, lat_pred, lon_pred, fut, op_mask,avg_along_time = True)
avg_val_lat_acc += (torch.sum(torch.max(lat_pred.data, 1)[1] == torch.max(lat_enc.data, 1)[1])).item() / lat_enc.size()[0]
avg_val_lon_acc += (torch.sum(torch.max(lon_pred.data, 1)[1] == torch.max(lon_enc.data, 1)[1])).item() / lon_enc.size()[0]
else:
fut_pred = net(hist, nbrs, mask, lat_enc, lon_enc)
if epoch_num < pretrainEpochs:
l = maskedMSE(fut_pred, fut, op_mask)
else:
l = maskedNLL(fut_pred, fut, op_mask)
avg_val_loss += l.item()
val_batch_count += 1
print(avg_val_loss/val_batch_count)
# Print validation loss and update display variables
print('Validation loss :',format(avg_val_loss/val_batch_count,'0.4f'),"| Val Acc:",format(avg_val_lat_acc/val_batch_count*100,'0.4f'),format(avg_val_lon_acc/val_batch_count*100,'0.4f'))
val_loss.append(avg_val_loss/val_batch_count)
prev_val_loss = avg_val_loss/val_batch_count
#__________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________
torch.save(net.state_dict(), 'trained_models/base.tar')