def inicializar_pointnet():
print("Loading 3D Object Classification Model:")
start = time.time()
global classifier
classifier = PointNetCls(k=2) #IMP, 2 é o número de classes, depois alterar.
classifier.cuda()
#classifier.load_state_dict(torch.load('/home/socialab/FCHarDNet/cls_model_40.pth'))
classifier.load_state_dict(torch.load('/home/socialab/human_vision/FCHarDNet/cls_model_40.pth'))
classifier.eval()
end = time.time()
print(" (time): " + str(end-start))
loss += feature_transform_regularizer(trans_feat) * 0.001
loss.backward()
optimizer.step()
pred_choice = pred.data.max(1)[1]
correct = pred_choice.eq(target.data).cpu().sum()
print('[%d: %d/%d] train loss: %f accuracy: %f' %
(epoch, i, num_batch, loss.item(),
correct.item() / float(opt.batchSize)))
if i % 10 == 0:
j, data = next(enumerate(testdataloader, 0))
points, target = data
target = target[:, 0]
points = points.transpose(2, 1)
points, target = points.cuda(), target.cuda()
classifier = classifier.eval()
pred, _, _ = classifier(points)
loss = F.nll_loss(pred, target)
pred_choice = pred.data.max(1)[1]
correct = pred_choice.eq(target.data).cpu().sum()
print('[%d: %d/%d] %s loss: %f accuracy: %f' %
(epoch, i, num_batch, blue('test'), loss.item(),
correct.item() / float(opt.batchSize)))
torch.save(classifier.state_dict(),
'%s/cls_model_%d.pth' % (opt.outf, epoch))
total_correct = 0
total_testset = 0
for i, data in tqdm(enumerate(testdataloader, 0)):
points, target = data
os.makedirs(os.path.join('save', model_name, adv+'-'+str(eps)+'-'+str(int(n))+'-'+str(eps_iter)))
with open(os.path.join('dataset', 'random1024', 'whole_data_and_whole_label.pkl'), 'rb') as fid:
whole_data, whole_label = pkl.load(fid)
if model_name == 'PointNet':
from pointnet.model import PointNetCls
model = PointNetCls(k=40, feature_transform=True, predict_logit=True)
checkpoint = 'pointnet/cls_model_201.pth'
else:
print('No such model architecture')
assert False
model = model.to(device)
model.load_state_dict(torch.load(checkpoint))
model.eval()
pytorch_utils.requires_grad_(model, False)
print("Model name\t%s" % model_name)
cnt = 0 # adv pointcloud successfully attacked
CNT = 0 # clean pointcloud correctly classified
for idx in tqdm(range(len(whole_data))):
x = whole_data[idx]
label = whole_label[idx]
with torch.no_grad():
y_pred = model(torch.from_numpy(x[np.newaxis,:,:]).float().to(device))
y_pred_idx = np.argmax(y_pred.detach().cpu().numpy().flatten())
optimizer.step() # 梯度下降，参数优化
pred_choice = pred.data.max(1)[1] # max(1)返回每一行中的最大值及索引,[1]取出索引（代表着类别）
correct = pred_choice.eq(
target.data).cpu().sum() # 判断和target是否匹配，并计算匹配的数量
print('[%d: %d/%d] train loss: %f accuracy: %f' %
(epoch, i, num_batch, loss.item(),
correct.item() / float(opt.batchSize)))
# 每10次batch之后，进行一次测试
if i % 10 == 0:
j, data = next(enumerate(testdataloader, 0))
points, target = data
target = target[:, 0]
points = points.transpose(2, 1)
points, target = points.cuda(), target.cuda()
classifier = classifier.eval() # 测试模式，固定住BN和dropout
pred, _, _ = classifier(points)
loss = F.nll_loss(pred, target)
pred_choice = pred.data.max(1)[1]
correct = pred_choice.eq(target.data).cpu().sum()
print('[%d: %d/%d] %s loss: %f accuracy: %f' %
(epoch, i, num_batch, blue('test'), loss.item(),
correct.item() / float(opt.batchSize)))
torch.save(classifier.state_dict(),
'%s/cls_model_%d.pth' % (opt.outf, epoch))
total_correct = 0
total_testset = 0
for i, data in tqdm(enumerate(testdataloader, 0)):
points, target = data
end_time = time.time()
print("classification~ cost:{:.1f}ms ".format(
(end_time - start_time) * 1000))
print('-------------------------------------')
def listener():
# In ROS, nodes are uniquely named. If two nodes with the same
# name are launched, the previous one is kicked off. The
# anonymous=True flag means that rospy will choose a unique
# name for our 'listener' node so that multiple listeners can
# run simultaneously.
rospy.init_node('obj_classify', anonymous=True)
rospy.Subscriber("fused_lidar", pc2, callback)
global pub
pub = rospy.Publisher("classification", pc2, queue_size=20)
# spin() simply keeps python from exiting until this node is stopped
rospy.spin()
if __name__ == '__main__':
# 网络加载
classifier = PointNetCls(k=num_classes) # 训练模型为16类
classifier.cuda()
classifier.load_state_dict(torch.load(path_model))
classifier.eval() # 测试模式
pub = None
listener()
def our_main():
from utils.show3d_balls import showpoints
parser = argparse.ArgumentParser()
parser.add_argument(
'--batchSize', type=int, default=32, help='input batch size')
parser.add_argument(
'--num_points', type=int, default=2000, help='input batch size')
parser.add_argument(
'--workers', type=int, help='number of data loading workers', default=4)
parser.add_argument(
'--nepoch', type=int, default=250, help='number of epochs to train for')
parser.add_argument('--outf', type=str, default='cls', help='output folder')
parser.add_argument('--model', type=str, default='', help='model path')
parser.add_argument('--dataset', type=str, required=True, help="dataset path")
parser.add_argument('--dataset_type', type=str, default='shapenet', help="dataset type shapenet|modelnet40")
parser.add_argument('--feature_transform', action='store_true', help="use feature transform")
opt = parser.parse_args()
print(opt)
blue = lambda x: '\033[94m' + x + '\033[0m'
opt.manualSeed = random.randint(1, 10000) # fix seed
print("Random Seed: ", opt.manualSeed)
random.seed(opt.manualSeed)
torch.manual_seed(opt.manualSeed)
if opt.dataset_type == 'shapenet':
dataset = ShapeNetDataset(
root=opt.dataset,
classification=True,
npoints=opt.num_points)
test_dataset = ShapeNetDataset(
root=opt.dataset,
classification=True,
split='test',
npoints=opt.num_points,
data_augmentation=False)
elif opt.dataset_type == 'modelnet40':
dataset = ModelNetDataset(
root=opt.dataset,
npoints=opt.num_points,
split='trainval')
test_dataset = ModelNetDataset(
root=opt.dataset,
split='test',
npoints=opt.num_points,
data_augmentation=False)
else:
exit('wrong dataset type')
dataloader = torch.utils.data.DataLoader(
dataset,
batch_size=opt.batchSize,
shuffle=True,
num_workers=int(opt.workers))
testdataloader = torch.utils.data.DataLoader(
test_dataset,
batch_size=opt.batchSize,
shuffle=True,
num_workers=int(opt.workers))
print(len(dataset), len(test_dataset))
num_classes = len(dataset.classes)
print('classes', num_classes)
try:
os.makedirs(opt.outf)
except OSError:
pass
classifier = PointNetCls(k=num_classes, feature_transform=opt.feature_transform)
if opt.model != '':
classifier.load_state_dict(torch.load(opt.model))
optimizer = optim.Adam(classifier.parameters(), lr=0.001, betas=(0.9, 0.999))
scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=20, gamma=0.5)
classifier.cuda()
num_batch = len(dataset) / opt.batchSize
## python train_classification.py --dataset ../dataset --nepoch=4 --dataset_type shapenet
for epoch in range(opt.nepoch):
scheduler.step()
for i, data in enumerate(dataloader, 0):
points, target = data
target = target[:, 0]
showpoints(points[0].numpy())
points = points.transpose(2, 1)
points, target = points.cuda(), target.cuda()
optimizer.zero_grad()
classifier = classifier.train()
pred, trans, trans_feat = classifier(points)
loss = F.nll_loss(pred, target)
if opt.feature_transform:
loss += feature_transform_regularizer(trans_feat) * 0.001
loss.backward()
optimizer.step()
pred_choice = pred.data.max(1)[1]
correct = pred_choice.eq(target.data).cpu().sum()
print('[%d: %d/%d] train loss: %f accuracy: %f' % (epoch, i, num_batch, loss.item(), correct.item() / float(opt.batchSize)))
if i % 10 == 0:
j, data = next(enumerate(testdataloader, 0))
points, target = data
target = target[:, 0]
points = points.transpose(2, 1)
points, target = points.cuda(), target.cuda()
classifier = classifier.eval()
pred, _, _ = classifier(points)
loss = F.nll_loss(pred, target)
pred_choice = pred.data.max(1)[1]
correct = pred_choice.eq(target.data).cpu().sum()
print('[%d: %d/%d] %s loss: %f accuracy: %f' % (epoch, i, num_batch, blue('test'), loss.item(), correct.item()/float(opt.batchSize)))
torch.save(classifier.state_dict(), '%s/cls_model_%d.pth' % (opt.outf, epoch))
total_correct = 0
total_testset = 0
for i,data in tqdm(enumerate(testdataloader, 0)):
points, target = data
target = target[:, 0]
points = points.transpose(2, 1)
points, target = points.cuda(), target.cuda()
classifier = classifier.eval()
pred, _, _ = classifier(points)
pred_choice = pred.data.max(1)[1]
correct = pred_choice.eq(target.data).cpu().sum()
total_correct += correct.item()
total_testset += points.size()[0]
print("final accuracy {}".format(total_correct / float(total_testset)))
if opt.feature_transform:
loss += feature_transform_regularizer(trans_feat) * 0.001
loss.backward() # 小批量的损失对模型参数求梯度
optimizer.step() # 梯度下降优化 以batch为单位, 通过调用optim实例的step函数来迭代模型参数, w, b
pred_choice = pred.data.max(1)[1]
correct = pred_choice.eq(target.data).cpu().sum().item()
# n += target.shape[0]
print('[%d: %d/%d] train loss: %f accuracy: %f' % (epoch, i, num_batch, loss.item(), correct / float(opt.batchSize)))
if i % 10 == 0: # 每10个batch执行一次,即为每10*batchSize个点云执行一次验证
j, data = next(enumerate(testdataloader, 0))
points, target = data
target = target[:, 0]
points = points.transpose(2, 1)
points, target = points.cuda(), target.cuda() # 复制到cuda上进行计算
classifier = classifier.eval() # 模型设置为评估模式
pred, _, _ = classifier(points)
# print(pred.shape) # torch.Size([32, 16]) 输入有32个点云模型, 输出就有32*16大小的矩阵, 32行代表32个点云模型, 每行16个值代表可能属于16个类别的分数
loss = F.nll_loss(pred, target)
pred_choice = pred.data.max(1)[1] # 按行寻找最大值的位置
correct = pred_choice.eq(target.data).cpu().sum().item()
print('[%d: %d/%d] %s loss: %f accuracy: %f' % (epoch, i, num_batch, blue('test'), loss.item(), correct/float(opt.batchSize)))
# print('n = ', n)
torch.save(classifier.state_dict(), '%s/cls_model_%d.pth' % (opt.outf, epoch)) # 每个epoch都保存一个模型
total_correct = 0
total_testset = 0
for i,data in tqdm(enumerate(testdataloader, 0)): # enumerate在字典上是枚举、列举的意思, enumerate参数为可遍历/可迭代的对象(如列表、字符串), 后面的0是索引从0开始
points, target = data
target = target[:, 0]
