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train_partseg.py
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train_partseg.py
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import torch
import torch.optim as optim
import torch.optim.lr_scheduler as lr_sched
import torch.nn as nn
from torch.utils.data import DataLoader
from torch.autograd import Variable
import numpy as np
import os
from torchvision import transforms
from models import RSCNN_MSN_Seg as RSCNN_MSN
from data import ShapeNetPart
import utils.pytorch_utils as pt_utils
import data.data_utils as d_utils
import argparse
import random
import yaml
torch.backends.cudnn.enabled = True
torch.backends.cudnn.benchmark = True
torch.backends.cudnn.deterministic = True
seed = 123
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
parser = argparse.ArgumentParser(description='Relation-Shape CNN Shape Part Segmentation Training')
parser.add_argument('--config', default='cfgs/config_msn_partseg.yaml', type=str)
def main():
args = parser.parse_args()
with open(args.config) as f:
config = yaml.load(f)
print("\n**************************")
for k, v in config['common'].items():
setattr(args, k, v)
print('\n[%s]:'%(k), v)
print("\n**************************\n")
try:
os.makedirs(args.save_path)
except OSError:
pass
train_transforms = transforms.Compose([
d_utils.PointcloudToTensor()
])
test_transforms = transforms.Compose([
d_utils.PointcloudToTensor()
])
train_dataset = ShapeNetPart(root = args.data_root, num_points = args.num_points, split = 'trainval', normalize = True, transforms = train_transforms)
train_dataloader = DataLoader(
train_dataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=int(args.workers),
pin_memory=True
)
global test_dataset
test_dataset = ShapeNetPart(root = args.data_root, num_points = args.num_points, split = 'test', normalize = True, transforms = test_transforms)
test_dataloader = DataLoader(
test_dataset,
batch_size=args.batch_size,
shuffle=False,
num_workers=int(args.workers),
pin_memory=True
)
model = RSCNN_MSN(num_classes = args.num_classes, input_channels = args.input_channels, relation_prior = args.relation_prior, use_xyz = True)
model.cuda()
optimizer = optim.Adam(
model.parameters(), lr=args.base_lr, weight_decay=args.weight_decay)
lr_lbmd = lambda e: max(args.lr_decay**(e // args.decay_step), args.lr_clip / args.base_lr)
bnm_lmbd = lambda e: max(args.bn_momentum * args.bn_decay**(e // args.decay_step), args.bnm_clip)
lr_scheduler = lr_sched.LambdaLR(optimizer, lr_lbmd)
bnm_scheduler = pt_utils.BNMomentumScheduler(model, bnm_lmbd)
if args.checkpoint is not '':
model.load_state_dict(torch.load(args.checkpoint))
print('Load model successfully: %s' % (args.checkpoint))
criterion = nn.CrossEntropyLoss()
num_batch = len(train_dataset)/args.batch_size
# training
train(train_dataloader, test_dataloader, model, criterion, optimizer, lr_scheduler, bnm_scheduler, args, num_batch)
def train(train_dataloader, test_dataloader, model, criterion, optimizer, lr_scheduler, bnm_scheduler, args, num_batch):
PointcloudScaleAndTranslate = d_utils.PointcloudScaleAndTranslate() # initialize augmentation
global Class_mIoU, Inst_mIoU
Class_mIoU, Inst_mIoU = 0.83, 0.85
batch_count = 0
model.train()
for epoch in range(args.epochs):
for i, data in enumerate(train_dataloader, 0):
if lr_scheduler is not None:
lr_scheduler.step(epoch)
if bnm_scheduler is not None:
bnm_scheduler.step(epoch-1)
points, target, cls = data
points, target = points.cuda(), target.cuda()
points, target = Variable(points), Variable(target)
# augmentation
points.data = PointcloudScaleAndTranslate(points.data)
optimizer.zero_grad()
batch_one_hot_cls = np.zeros((len(cls), 16)) # 16 object classes
for b in range(len(cls)):
batch_one_hot_cls[b, int(cls[b])] = 1
batch_one_hot_cls = torch.from_numpy(batch_one_hot_cls)
batch_one_hot_cls = Variable(batch_one_hot_cls.float().cuda())
pred = model(points, batch_one_hot_cls)
pred = pred.view(-1, args.num_classes)
target = target.view(-1,1)[:,0]
loss = criterion(pred, target)
loss.backward()
optimizer.step()
if i % args.print_freq_iter == 0:
print('[epoch %3d: %3d/%3d] \t train loss: %0.6f \t lr: %0.5f' %(epoch+1, i, num_batch, loss.data.clone(), lr_scheduler.get_lr()[0]))
batch_count += 1
# validation in between an epoch
if (epoch < 3 or epoch > 40) and args.evaluate and batch_count % int(args.val_freq_epoch * num_batch) == 0:
validate(test_dataloader, model, criterion, args, batch_count)
def validate(test_dataloader, model, criterion, args, iter):
global Class_mIoU, Inst_mIoU, test_dataset
model.eval()
seg_classes = test_dataset.seg_classes
shape_ious = {cat:[] for cat in seg_classes.keys()}
seg_label_to_cat = {} # {0:Airplane, 1:Airplane, ...49:Table}
for cat in seg_classes.keys():
for label in seg_classes[cat]:
seg_label_to_cat[label] = cat
losses = []
for _, data in enumerate(test_dataloader, 0):
points, target, cls = data
points, target = Variable(points, volatile=True), Variable(target, volatile=True)
points, target = points.cuda(), target.cuda()
batch_one_hot_cls = np.zeros((len(cls), 16)) # 16 object classes
for b in range(len(cls)):
batch_one_hot_cls[b, int(cls[b])] = 1
batch_one_hot_cls = torch.from_numpy(batch_one_hot_cls)
batch_one_hot_cls = Variable(batch_one_hot_cls.float().cuda())
pred = model(points, batch_one_hot_cls)
loss = criterion(pred.view(-1, args.num_classes), target.view(-1,1)[:,0])
losses.append(loss.data.clone())
pred = pred.data.cpu()
target = target.data.cpu()
pred_val = torch.zeros(len(cls), args.num_points).type(torch.LongTensor)
# pred to the groundtruth classes (selected by seg_classes[cat])
for b in range(len(cls)):
cat = seg_label_to_cat[target[b, 0]]
logits = pred[b, :, :] # (num_points, num_classes)
pred_val[b, :] = logits[:, seg_classes[cat]].max(1)[1] + seg_classes[cat][0]
for b in range(len(cls)):
segp = pred_val[b, :]
segl = target[b, :]
cat = seg_label_to_cat[segl[0]]
part_ious = [0.0 for _ in range(len(seg_classes[cat]))]
for l in seg_classes[cat]:
if torch.sum((segl == l) | (segp == l)) == 0:
# part is not present in this shape
part_ious[l - seg_classes[cat][0]] = 1.0
else:
part_ious[l - seg_classes[cat][0]] = torch.sum((segl == l) & (segp == l)) / float(torch.sum((segl == l) | (segp == l)))
shape_ious[cat].append(np.mean(part_ious))
instance_ious = []
for cat in shape_ious.keys():
for iou in shape_ious[cat]:
instance_ious.append(iou)
shape_ious[cat] = np.mean(shape_ious[cat])
mean_class_ious = np.mean(list(shape_ious.values()))
for cat in sorted(shape_ious.keys()):
print('****** %s: %0.6f'%(cat, shape_ious[cat]))
print('************ Test Loss: %0.6f' % (np.array(losses).mean()))
print('************ Class_mIoU: %0.6f' % (mean_class_ious))
print('************ Instance_mIoU: %0.6f' % (np.mean(instance_ious)))
if mean_class_ious > Class_mIoU or np.mean(instance_ious) > Inst_mIoU:
if mean_class_ious > Class_mIoU:
Class_mIoU = mean_class_ious
if np.mean(instance_ious) > Inst_mIoU:
Inst_mIoU = np.mean(instance_ious)
torch.save(model.state_dict(), '%s/seg_msn_iter_%d_ins_%0.6f_cls_%0.6f.pth' % (args.save_path, iter, np.mean(instance_ious), mean_class_ious))
model.train()
if __name__ == "__main__":
main()