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train_tracking_waymo.py
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train_tracking_waymo.py
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import argparse
import os
import random
import time
import logging
import pdb
from tqdm import tqdm
import numpy as np
import scipy.io as sio
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.optim as optim
import torch.optim.lr_scheduler as lr_scheduler
import torch.utils.data
import torch.nn.functional as F
from torch.autograd import Variable
from waymo_dataset import WaymoSiameseDataset
from pointnet2.models import get_model
parser = argparse.ArgumentParser()
parser.add_argument('--batchSize', type=int, default=64, help='input batch size')
parser.add_argument('--workers', type=int, default=8, help='number of data loading workers')
parser.add_argument('--nepoch', type=int, default=600, help='number of epochs to train for')
parser.add_argument('--learning_rate', type=float, default=0.001, help='learning rate at t=0')
parser.add_argument('--input_feature_num', type=int, default = 0, help='number of input point features')
parser.add_argument('--data_dir', type=str, default = './data/waymo_tracking', help='dataset path')
parser.add_argument('--category_name', type=str, default = 'Vehicle', help='Object to Track (Vehicle/Pedestrian/Cyclist)')
parser.add_argument('--save_root_dir', type=str, default='results', help='output folder')
parser.add_argument('--model', type=str, default = '', help='model name for training resume')
parser.add_argument('--optimizer', type=str, default = '', help='optimizer name for training resume')
parser.add_argument('--tiny', type=bool, default=False)
parser.add_argument('--input_size', type=int, default=1024)
parser.add_argument('--save_interval', type=int, default=20)
opt = parser.parse_args()
print(opt)
opt.manualSeed = 1
random.seed(opt.manualSeed)
torch.manual_seed(opt.manualSeed)
save_dir = opt.save_root_dir
if not os.path.isdir(save_dir):
os.makedirs(save_dir)
logging.basicConfig(format='%(asctime)s %(message)s', datefmt='%Y/%m/%d %H:%M:%S', \
filename=os.path.join(save_dir, 'train.log'), level=logging.INFO)
logging.info('======================================================')
# 1. Load data
train_data = WaymoSiameseDataset(
input_size=opt.input_size,
path=os.path.join(opt.data_dir, 'train'),
category_name=opt.category_name,
min_seq_len=2,
min_pts_in_gt=10,
offset_BB=0.1,
scale_BB=1.0) # opt.scale)
train_dataloader = torch.utils.data.DataLoader(
train_data,
batch_size=opt.batchSize,
shuffle=True,
num_workers=int(opt.workers),
pin_memory=True)
test_data = WaymoSiameseDataset(
input_size=opt.input_size,
path=os.path.join(opt.data_dir, 'test'),
category_name=opt.category_name,
min_seq_len=10,
min_pts_in_gt=10,
offset_BB=0.1,
scale_BB=1.0)
test_dataloader = torch.utils.data.DataLoader(
test_data,
batch_size=int(opt.batchSize // 2),
shuffle=False,
num_workers=int(opt.workers),
pin_memory=True)
print('#Train data:', len(train_data), '#Test data:', len(test_data))
# 2. Define model, loss and optimizer
netR = get_model(name='T', # opt.type,
input_channels=opt.input_feature_num,
use_xyz=True,
input_size=opt.input_size)
netR = torch.nn.DataParallel(netR)
if opt.model != '':
netR.load_state_dict(torch.load(os.path.join(save_dir, opt.model)))
netR.cuda()
criterion_cla = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([1.0])).cuda()
criterion_reg = nn.MSELoss(reduction='none').cuda()
criterion_objective = nn.BCEWithLogitsLoss(
pos_weight=torch.tensor([2.0]),
reduction='none').cuda()
criterion_box = nn.MSELoss(reduction='none').cuda()
optimizer = optim.Adam(netR.parameters(),
lr=opt.learning_rate,
betas = (0.5, 0.999),
eps=1e-6)
if opt.optimizer != '':
optimizer.load_state_dict(torch.load(os.path.join(save_dir, opt.optimizer)))
scheduler = lr_scheduler.StepLR(optimizer, step_size=200, gamma=0.2)
def one_sample_step(input_dict, model, optimizer, train=True):
# 3.1.2 compute output
optimizer.zero_grad()
output_dict = model(input_dict)
label_cla = output_dict['cls_label']
label_reg = output_dict['reg_label']
estimation_cla = output_dict['estimation_cla']
estimation_reg = output_dict['estimation_reg'] # vote xyz
estimation_box = output_dict['estimation_box']
center_xyz = output_dict['center_xyz'] # candi
loss_cla = criterion_cla(estimation_cla, label_cla)
# vote -> box center
loss_reg = criterion_reg(estimation_reg, label_reg[:, :, 0:3]) # 16x128x3
loss_reg = (loss_reg.mean(2) * label_cla).sum() / (label_cla.sum() + 1e-06)
K = center_xyz.shape[1]
dist = torch.sum((center_xyz - label_reg[:, :, 0:3]) ** 2, dim=-1)
dist = torch.sqrt(dist + 1e-6)
B = dist.size(0)
objectness_mask = torch.ones((B, K), requires_grad=False).float().cuda()
objectness_label = label_cla
box_mask = label_cla
loss_objective = criterion_objective(estimation_box[:, :, 4], objectness_label)
loss_objective = torch.sum(loss_objective * objectness_mask) / (torch.sum(objectness_mask) + 1e-6)
loss_box = criterion_box(estimation_box[:, :, 0:4], label_reg[:, 0:K, :])
loss_box = (loss_box.mean(2) * box_mask).sum() / (box_mask.sum() + 1e-06)
loss = loss_cla + loss_reg + 1.0 * loss_box + 1.0 * loss_objective
if train:
loss.backward()
optimizer.step()
# 3.1.4 update training error
estimation_cla_cpu = estimation_cla.sigmoid().detach().cpu().numpy()
label_cla_cpu = label_cla.detach().cpu().numpy()
correct = float(np.sum((
estimation_cla_cpu > 0.4) == label_cla_cpu)
) / label_cla_cpu.size
true_correct = float(np.sum(
(np.float32(estimation_cla_cpu > 0.4)
+ label_cla_cpu) == 2)) \
/ np.sum(label_cla_cpu)
return {
'correct' : correct,
'true_correct' : true_correct,
'loss_cla' : loss_cla,
'loss_reg' : loss_reg,
'loss' : loss
}
# 3. Training and testing
for epoch in range(opt.nepoch):
scheduler.step(epoch)
print('======>>>>> Online epoch: #%d, lr=%f <<<<<======' %(epoch, scheduler.get_lr()[0]))
# 3.1 switch to train mode
# torch.cuda.synchronize()
netR.train()
train_mse = 0.0
timer = time.time()
batch_correct = 0.0
batch_cla_loss = 0.0
batch_reg_loss = 0.0
batch_box_loss = 0.0
batch_num = 0.0
batch_iou = 0.0
batch_true_correct = 0.0
for i, input_dict in enumerate(tqdm(train_dataloader, 0)):
if len(input_dict['search']) == 1:
continue
# torch.cuda.synchronize()
# 3.1.1 load inputs and targets
for k, v in input_dict.items():
input_dict[k] = Variable(v, requires_grad=False).cuda()
output_dict = one_sample_step(input_dict, netR, optimizer)
correct = output_dict['correct']
true_correct = output_dict['true_correct']
loss_cla = output_dict['loss_cla']
loss_reg = output_dict['loss_reg']
loss = output_dict['loss']
train_mse = train_mse + loss.data * len(input_dict['search'])
batch_correct += correct
batch_cla_loss += loss_cla.data
batch_reg_loss += loss_reg.data
batch_num += 1 # len(input_dict['search'])
batch_true_correct += true_correct
if (i + 1) % 20 == 0:
print('\n ---- batch: %03d ----' % (i+1))
print('cla_loss: %f, reg_loss: %f, box_loss: %f' %
(batch_cla_loss/20,batch_reg_loss/20,batch_box_loss/20))
print('accuracy: %f' % (batch_correct / float(batch_num)))
print('true accuracy: %f' % (batch_true_correct / float(batch_num)))
batch_correct = 0.0
batch_cla_loss = 0.0
batch_reg_loss = 0.0
batch_box_loss = 0.0
batch_num = 0.0
batch_true_correct = 0.0
# time taken
train_mse = train_mse / len(train_data)
# torch.cuda.synchronize()
timer = time.time() - timer
timer = timer / len(train_data)
print('==> time to learn 1 sample = %f (ms)' %(timer*1000))
if epoch and (epoch % opt.save_interval == 0 or epoch == opt.nepoch-1):
torch.save(netR.state_dict(), '%s/netR_%d.pth' % (save_dir, epoch))
# 3.2 switch to evaluate mode
netR.eval()
test_cla_loss = 0.0
test_reg_loss = 0.0
test_box_loss = 0.0
test_correct = 0.0
test_true_correct = 0.0
timer = time.time()
for i, data in enumerate(tqdm(test_dataloader, 0)):
for k, v in input_dict.items():
input_dict[k] = Variable(v, requires_grad=False).cuda()
with torch.no_grad():
output_dict = one_sample_step(input_dict, netR, optimizer, train=False)
correct = output_dict['correct']
true_correct = output_dict['true_correct']
test_correct += correct
test_true_correct += true_correct
# time taken
timer = time.time() - timer
timer = timer / len(test_data)
print('==> time to learn 1 sample = %f (ms)' %(timer*1000))
# print mse
test_cla_loss = test_cla_loss / len(test_data)
test_reg_loss = test_reg_loss / len(test_data)
test_box_loss = test_box_loss / len(test_data)
print('cla_loss: %f, reg_loss: %f, box_loss: %f, #test_data = %d' %
(test_cla_loss, test_reg_loss, test_box_loss, len(test_data)))
test_correct = test_correct / len(test_dataloader)
print('mean-correct of 1 sample: %f, #test_data = %d' %(test_correct, len(test_data)))
test_true_correct = test_true_correct / len(test_dataloader)
print('true correct of 1 sample: %f' %(test_true_correct))
# log
logging.info('Epoch#%d: train error=%e, test error=%e,%e,%e, test correct=%e, %e, lr = %f' %
(epoch, train_mse, test_cla_loss, test_reg_loss, test_box_loss,
test_correct, test_true_correct, scheduler.get_lr()[0]))