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train_pvcnn.py
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import numpy as np
from tqdm import tqdm
from tensorboardX import SummaryWriter
from shutil import rmtree
import torch, argparse
torch.backends.cudnn.benchmark = True
from torch.nn import CrossEntropyLoss
from torch.optim import Adam
from torch.utils.data import DataLoader
from torch import nn
from open3d.ml.torch.models import PVCNN
from dataloaders.dicea_open3d import DICEADatasetOpen3D
from util.metrics import Metrics
from util.common_util import schedule, log_pcs
#set seed for reproducibility
seed = 12345
np.random.seed(seed)
torch.manual_seed(seed)
def conv_to_tensor(inputs):
data = {}
for k,v in inputs.items():
if k == "coords" or k == "neighbor_indices" or k == "sub_idx" or k == "interp_idx":
new = [torch.from_numpy(t) for t in v]
data = {**data,**{k:new}}
else:
data = {**data,**{k:torch.from_numpy(v)}}
return data
###### VALIDATION
def validate(writer, vset, vloader, epoch, model, device): #PA, PP, mIoU
metric = Metrics(vset.cnames[1:], device=device)
model.eval()
with torch.no_grad():
for idx, data in enumerate(tqdm(vloader, "Validating Epoch %d" % (epoch + 1), total=len(vset))):
o = model(data)
y = data["label"].cuda()
metric.add_sample(o.argmax(dim=2).detach().flatten(), y.flatten())
miou = metric.percent_mIoU()
acc = metric.percent_acc()
prec = metric.percent_prec()
writer.add_scalar('mIoU', miou, epoch)
writer.add_scalar('PP', prec, epoch)
writer.add_scalar('PA', acc, epoch)
writer.add_scalars('IoU', {n:100*v for n,v in zip(metric.name_classes, metric.IoU()) if not torch.isnan(v)}, epoch)
print(metric)
model.train()
return miou, o.swapaxes(1,2), y
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument("--epochs", type=int, default=5000, help='number of epochs to run')
parser.add_argument("--batch_size", type=int, default=8, help='batch_size')
parser.add_argument("--cube_edge", type=int, default=96, help='granularity of voxelization train')
parser.add_argument("--val_cube_edge", type=int, default=96, help='granularity of voxelization val')
parser.add_argument("--num_classes", type=int, default=8, help='number of classes to consider')
parser.add_argument("--dset_path", type=str, default="/home/elena/Documents/Hepic", help='dataset path')
parser.add_argument("--test_name", type=str, default='test', help='optional test name')
parser.add_argument("--pretrain", type=str, help='pretrained model path')
parser.add_argument("--loss", choices=['ce','cwce','ohem','mixed'], default='mixed', type=str, help='which loss to use')
args = parser.parse_args()
lr0 = 2.5e-4
lre = 1e-5
eval_every_n_epochs = 10
device = 'cuda' if torch.cuda.is_available() else 'cpu'
logdir = "log/train_pcs" + "_" + args.test_name
rmtree(logdir, ignore_errors=True)
writer = SummaryWriter(logdir, flush_secs=.5)
# Load model
model = PVCNN(num_classes=args.num_classes)
if args.pretrain:
new = model.state_dict()
old = torch.load(args.pretrain)
for k in new:
if "out" not in k:
new[k] = old[k]
model.load_state_dict(new)
print("model restored from ", args.pretrain)
model.to('cuda')
#Load dataset
dataset = DICEADatasetOpen3D
dset = dataset(root_path=args.dset_path,
model=PVCNN,
cube_edge=args.cube_edge)
dloader = DataLoader(dset,
batch_size=args.batch_size,
shuffle=True,
num_workers=4,
drop_last=True)
vset = dataset(root_path=args.dset_path,
cube_edge=args.val_cube_edge,
model=PVCNN,
#augment=False,
split='val')
vloader = DataLoader(vset,
batch_size=1,
shuffle=False,
num_workers=4)
# set up parameters for training
steps_per_epoch = len(dset)//args.batch_size
tot_steps = steps_per_epoch*args.epochs
optim = Adam(model.parameters(), weight_decay=1e-5)
# to visualize point cloud
pts = 2*torch.from_numpy(np.indices((args.val_cube_edge, args.val_cube_edge, args.val_cube_edge))
.reshape(3, -1).T).unsqueeze(0)/args.cube_edge - 1.
best_miou = 0
for e in range(args.epochs):
torch.cuda.empty_cache()
# Validation every n epochs
if e % eval_every_n_epochs == 0:
if e>=0:
miou, o, y = validate(writer, vset, vloader, e, model, device)
if miou>best_miou:
best_miou = miou
torch.save(model.state_dict(), logdir+"/val_best.pth")
#log_pcs(writer, pts, o, y)
metrics = Metrics(dset.cnames[1:], device=device)
pbar = tqdm(dloader, total=steps_per_epoch, desc="Epoch %d/%d, Loss: %.2f, mIoU: %.2f, Progress"%(e+1, args.epochs, 0., 0.))
for i, data in enumerate(pbar):
step = i+steps_per_epoch*e
lam = schedule(10, 1, step, tot_steps//2, .9)
loss = nn.CrossEntropyLoss(ignore_index=-1)
lr = schedule(lr0, lre, step, tot_steps, .9)
optim.param_groups[0]['lr'] = lr
optim.zero_grad()
o = model(data)
y = data["label"].cuda()
l = loss(o.swapaxes(1,2), y.long())
l.backward()
metrics.add_sample(o.detach().argmax(dim=2).flatten(), y.flatten())
optim.step()
miou = metrics.percent_mIoU()
pbar.set_description("Epoch %d/%d, Loss: %.2f, mIoU: %.2f, Progress"%(e+1, args.epochs, l.item(), miou))
writer.add_scalar('lr', lr, step)
writer.add_scalar('loss', l.item(), step)
writer.add_scalar('step_mIoU', miou, step)
torch.save(model.state_dict(), logdir+"/latest.pth")
# EVALUATION
miou, o, y = validate(writer, vset, vloader, e, model, device)
if miou>best_miou:
best_miou = miou
torch.save(model.state_dict(), logdir+"/val_best.pth")