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train_tracking.py
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train_tracking.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 Dataset import SiameseTrain
from pointnet2.models import Pointnet_Tracking
parser = argparse.ArgumentParser()
parser.add_argument('--batchSize', type=int, default=48, help='input batch size')
parser.add_argument('--workers', type=int, default=0, help='number of data loading workers')
parser.add_argument('--nepoch', type=int, default=60, help='number of epochs to train for')
parser.add_argument('--ngpu', type=int, default=2, help='# GPUs')
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/training', help='dataset path')
parser.add_argument('--category_name', type=str, default = 'Car', help='Object to Track (Car/Pedetrian/Van/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')
opt = parser.parse_args()
print (opt)
#torch.cuda.set_device(opt.main_gpu)
os.environ["CUDA_VISIBLE_DEVICES"] = '0,1'
opt.manualSeed = 1
random.seed(opt.manualSeed)
torch.manual_seed(opt.manualSeed)
save_dir = opt.save_root_dir
try:
os.makedirs(save_dir)
except OSError:
pass
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 = SiameseTrain(
input_size=1024,
path= opt.data_dir,
split='Train',
category_name=opt.category_name,
offset_BB=0,
scale_BB=1.25)
train_dataloader = torch.utils.data.DataLoader(
train_data,
batch_size=opt.batchSize,
shuffle=True,
num_workers=int(opt.workers),
pin_memory=True)
test_data = SiameseTrain(
input_size=1024,
path=opt.data_dir,
split='Valid',
category_name=opt.category_name,
offset_BB=0,
scale_BB=1.25)
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))
print (opt)
# 2. Define model, loss and optimizer
netR = Pointnet_Tracking(input_channels=opt.input_feature_num, use_xyz=True)
if opt.ngpu > 1:
netR = torch.nn.DataParallel(netR, range(opt.ngpu))
if opt.model != '':
netR.load_state_dict(torch.load(os.path.join(save_dir, opt.model)))
netR.cuda()
print(netR)
criterion_cla = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([1.0])).cuda()
criterion_reg = nn.SmoothL1Loss(reduction='none').cuda()
criterion_objective = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([2.0]), reduction='none').cuda()
criterion_box = nn.SmoothL1Loss(reduction='none').cuda()
optimizer = optim.Adam(netR.parameters(), lr=opt.learning_rate, betas = (0.5, 0.999), eps=1e-06)
if opt.optimizer != '':
optimizer.load_state_dict(torch.load(os.path.join(save_dir, opt.optimizer)))
scheduler = lr_scheduler.StepLR(optimizer, step_size=12, gamma=0.2)
# 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, data in enumerate(tqdm(train_dataloader, 0)):
if len(data[0]) == 1:
continue
torch.cuda.synchronize()
# 3.1.1 load inputs and targets
label_point_set, label_cla, label_reg, object_point_set = data
label_cla = Variable(label_cla, requires_grad=False).cuda()
label_reg = Variable(label_reg, requires_grad=False).cuda()
object_point_set = Variable(object_point_set, requires_grad=False).cuda()
label_point_set = Variable(label_point_set, requires_grad=False).cuda()
# 3.1.2 compute output
optimizer.zero_grad()
estimation_cla, estimation_reg,estimation_box,center_xyz = netR(object_point_set, label_point_set)
loss_cla = criterion_cla(estimation_cla, label_cla)
loss_reg = criterion_reg(estimation_reg, label_reg[:,:,0:3])
loss_reg = (loss_reg.mean(2) * label_cla).sum()/(label_cla.sum()+1e-06)
dist = torch.sum((center_xyz - label_reg[:,0:64,0:3])**2, dim=-1)
dist = torch.sqrt(dist+1e-6)
B = dist.size(0)
K = 64
objectness_label = torch.zeros((B,K), dtype=torch.float).cuda()
objectness_mask = torch.zeros((B,K)).cuda()
objectness_label[dist<0.3] = 1
objectness_mask[dist<0.3] = 1
objectness_mask[dist>0.6] = 1
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[:,0:64,:])
loss_box = (loss_box.mean(2) * objectness_label).sum()/(objectness_label.sum()+1e-06)
loss = 0.2*loss_cla + loss_reg + 1.5*loss_objective + 0.2*loss_box
# 3.1.3 compute gradient and do SGD step
loss.backward()
optimizer.step()
torch.cuda.synchronize()
# 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:len(label_point_set),:] > 0.4) == label_cla_cpu[0:len(label_point_set),:])) / 128.0
true_correct = float(np.sum((np.float32(estimation_cla_cpu[0:len(label_point_set),:] > 0.4) + label_cla_cpu[0:len(label_point_set),:]) == 2)/(np.sum(label_cla_cpu[0:len(label_point_set),:])))
train_mse = train_mse + loss.data*len(label_point_set)
batch_correct += correct
batch_cla_loss += loss_cla.data
batch_reg_loss += loss_reg.data
batch_box_loss += loss_box.data
batch_num += len(label_point_set)
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 / 20))
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))
torch.save(netR.state_dict(), '%s/netR_%d.pth' % (save_dir, epoch))
torch.save(optimizer.state_dict(), '%s/optimizer_%d.pth' % (save_dir, epoch))
# 3.2 switch to evaluate mode
torch.cuda.synchronize()
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)):
torch.cuda.synchronize()
# 3.2.1 load inputs and targets
label_point_set, label_cla, label_reg, object_point_set = data
label_cla = Variable(label_cla, requires_grad=False).cuda()
label_reg = Variable(label_reg, requires_grad=False).cuda()
object_point_set = Variable(object_point_set, requires_grad=False).cuda()
label_point_set = Variable(label_point_set, requires_grad=False).cuda()
# 3.2.2 compute output
estimation_cla, estimation_reg,estimation_box,center_xyz = netR(object_point_set, label_point_set)
loss_cla = criterion_cla(estimation_cla, label_cla)
loss_reg = criterion_reg(estimation_reg, label_reg[:,:,0:3])
loss_reg = (loss_reg.mean(2) * label_cla).sum()/(label_cla.sum()+1e-06)
dist = torch.sum((center_xyz - label_reg[:,0:64,0:3])**2, dim=-1)
dist = torch.sqrt(dist+1e-6)
B = dist.size(0)
K = 64
objectness_label = torch.zeros((B,K), dtype=torch.float).cuda()
objectness_mask = torch.zeros((B,K)).cuda()
objectness_label[dist<0.3] = 1
objectness_mask[dist<0.3] = 1
objectness_mask[dist>0.6] = 1
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[:,0:64,:])
loss_box = (loss_box.mean(2) * objectness_label).sum()/(objectness_label.sum()+1e-06)
loss = 0.2*loss_cla + loss_reg + 1.5*loss_objective + 0.2*loss_box
torch.cuda.synchronize()
test_cla_loss = test_cla_loss + loss_cla.data*len(label_point_set)
test_reg_loss = test_reg_loss + loss_reg.data*len(label_point_set)
test_box_loss = test_box_loss + loss_box.data*len(label_point_set)
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:len(label_point_set),:] > 0.4) == label_cla_cpu[0:len(label_point_set),:])) / 128.0
true_correct = float(np.sum((np.float32(estimation_cla_cpu[0:len(label_point_set),:] > 0.4) + label_cla_cpu[0:len(label_point_set),:]) == 2)/(np.sum(label_cla_cpu[0:len(label_point_set),:])))
test_correct += correct
test_true_correct += true_correct*len(label_point_set)
# time taken
torch.cuda.synchronize()
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_data)
print('mean-correct of 1 sample: %f, #test_data = %d' %(test_correct, len(test_data)))
test_true_correct = test_true_correct / len(test_data)
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]))