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train_partseg.py
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import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.utils.data import DataLoader
import numpy as np
import dgl
from dgl.data.utils import download, get_download_dir
from functools import partial
import tqdm
import urllib
import os
import argparse
import time
from ShapeNet import ShapeNet
from pointnet_partseg import PointNetPartSeg, PartSegLoss
from pointnet2_partseg import PointNet2MSGPartSeg, PointNet2SSGPartSeg
parser = argparse.ArgumentParser()
parser.add_argument('--model', type=str, default='pointnet')
parser.add_argument('--dataset-path', type=str, default='')
parser.add_argument('--load-model-path', type=str, default='')
parser.add_argument('--save-model-path', type=str, default='')
parser.add_argument('--num-epochs', type=int, default=250)
parser.add_argument('--num-workers', type=int, default=4)
parser.add_argument('--batch-size', type=int, default=16)
parser.add_argument('--tensorboard', action='store_true')
args = parser.parse_args()
num_workers = args.num_workers
batch_size = args.batch_size
def collate(samples):
graphs, cat = map(list, zip(*samples))
return dgl.batch(graphs), cat
CustomDataLoader = partial(
DataLoader,
num_workers=num_workers,
batch_size=batch_size,
shuffle=True,
drop_last=True)
def train(net, opt, scheduler, train_loader, dev):
category_list = sorted(list(shapenet.seg_classes.keys()))
eye_mat = np.eye(16)
net.train()
total_loss = 0
num_batches = 0
total_correct = 0
count = 0
start = time.time()
with tqdm.tqdm(train_loader, ascii=True) as tq:
for data, label, cat in tq:
num_examples = data.shape[0]
data = data.to(dev, dtype=torch.float)
label = label.to(dev, dtype=torch.long).view(-1)
opt.zero_grad()
cat_ind = [category_list.index(c) for c in cat]
# An one-hot encoding for the object category
cat_tensor = torch.tensor(eye_mat[cat_ind]).to(dev, dtype=torch.float).repeat(1, 2048)
cat_tensor = cat_tensor.view(num_examples, -1, 16).permute(0,2,1)
logits = net(data, cat_tensor)
loss = L(logits, label)
loss.backward()
opt.step()
_, preds = logits.max(1)
count += num_examples * 2048
loss = loss.item()
total_loss += loss
num_batches += 1
correct = (preds.view(-1) == label).sum().item()
total_correct += correct
AvgLoss = total_loss / num_batches
AvgAcc = total_correct / count
tq.set_postfix({
'AvgLoss': '%.5f' % AvgLoss,
'AvgAcc': '%.5f' % AvgAcc})
scheduler.step()
end = time.time()
return data, preds, AvgLoss, AvgAcc, end-start
def mIoU(preds, label, cat, cat_miou, seg_classes):
for i in range(preds.shape[0]):
shape_iou = 0
n = len(seg_classes[cat[i]])
for cls in seg_classes[cat[i]]:
pred_set = set(np.where(preds[i,:] == cls)[0])
label_set = set(np.where(label[i,:] == cls)[0])
union = len(pred_set.union(label_set))
inter = len(pred_set.intersection(label_set))
if union == 0:
shape_iou += 1
else:
shape_iou += inter / union
shape_iou /= n
cat_miou[cat[i]][0] += shape_iou
cat_miou[cat[i]][1] += 1
return cat_miou
def evaluate(net, test_loader, dev, per_cat_verbose=False):
category_list = sorted(list(shapenet.seg_classes.keys()))
eye_mat = np.eye(16)
net.eval()
cat_miou = {}
for k in shapenet.seg_classes.keys():
cat_miou[k] = [0, 0]
miou = 0
count = 0
per_cat_miou = 0
per_cat_count = 0
with torch.no_grad():
with tqdm.tqdm(test_loader, ascii=True) as tq:
for data, label, cat in tq:
num_examples = data.shape[0]
data = data.to(dev, dtype=torch.float)
label = label.to(dev, dtype=torch.long)
cat_ind = [category_list.index(c) for c in cat]
cat_tensor = torch.tensor(eye_mat[cat_ind]).to(dev, dtype=torch.float).repeat(1, 2048)
cat_tensor = cat_tensor.view(num_examples, -1, 16).permute(0,2,1)
logits = net(data, cat_tensor)
_, preds = logits.max(1)
cat_miou = mIoU(preds.cpu().numpy(),
label.view(num_examples, -1).cpu().numpy(),
cat, cat_miou, shapenet.seg_classes)
for _, v in cat_miou.items():
if v[1] > 0:
miou += v[0]
count += v[1]
per_cat_miou += v[0] / v[1]
per_cat_count += 1
tq.set_postfix({
'mIoU': '%.5f' % (miou / count),
'per Category mIoU': '%.5f' % (miou / count)})
if per_cat_verbose:
print("Per-Category mIoU:")
for k, v in cat_miou.items():
if v[1] > 0:
print("%s mIoU=%.5f" % (k, v[0] / v[1]))
else:
print("%s mIoU=%.5f" % (k, 1))
return miou / count, per_cat_miou / per_cat_count
dev = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# dev = "cpu"
if args.model == 'pointnet':
net = PointNetPartSeg(50, 3, 2048)
elif args.model == 'pointnet2_ssg':
net = PointNet2SSGPartSeg(50, batch_size, input_dims=6)
elif args.model == 'pointnet2_msg':
net = PointNet2MSGPartSeg(50, batch_size, input_dims=6)
net = net.to(dev)
if args.load_model_path:
net.load_state_dict(torch.load(args.load_model_path, map_location=dev))
opt = optim.Adam(net.parameters(), lr=0.001, weight_decay=1e-4)
scheduler = optim.lr_scheduler.StepLR(opt, step_size=20, gamma=0.5)
L = PartSegLoss()
shapenet = ShapeNet(2048, normal_channel=False)
train_loader = CustomDataLoader(shapenet.trainval())
test_loader = CustomDataLoader(shapenet.test())
# Tensorboard
if args.tensorboard:
import torchvision
from torch.utils.tensorboard import SummaryWriter
from torchvision import datasets, transforms
writer = SummaryWriter()
# Select 50 distinct colors for different parts
color_map = torch.tensor([
[47, 79, 79],[139, 69, 19],[112, 128, 144],[85, 107, 47],[139, 0, 0],[128, 128, 0],[72, 61, 139],[0, 128, 0],[188, 143, 143],[60, 179, 113],
[205, 133, 63],[0, 139, 139],[70, 130, 180],[205, 92, 92],[154, 205, 50],[0, 0, 139],[50, 205, 50],[250, 250, 250],[218, 165, 32],[139, 0, 139],
[10, 10, 10],[176, 48, 96],[72, 209, 204],[153, 50, 204],[255, 69, 0],[255, 145, 0],[0, 0, 205],[255, 255, 0],[0, 255, 0],[233, 150, 122],
[220, 20, 60],[0, 191, 255],[160, 32, 240],[192,192,192],[173, 255, 47],[218, 112, 214],[216, 191, 216],[255, 127, 80],[255, 0, 255],[100, 149, 237],
[128,128,128],[221, 160, 221],[144, 238, 144],[123, 104, 238],[255, 160, 122],[175, 238, 238],[238, 130, 238],[127, 255, 212],[255, 218, 185],[255, 105, 180],
])
# paint each point according to its pred
def paint(batched_points):
B, N = batched_points.shape
colored = color_map[batched_points].squeeze(2)
return colored
best_test_miou = 0
best_test_per_cat_miou = 0
# times = []
for epoch in range(args.num_epochs):
# start = time.time()
data, preds, AvgLoss, AvgAcc, training_time = train(net, opt, scheduler, train_loader, dev)
# end = time.time()
# t = end - start
# print("training time:", t)
# times.append(t)
# if epoch == 2:
# print("3 epochs avg:", sum(times) / len(times))
if (epoch + 1) % 5 == 0:
print('Epoch #%d Testing' % epoch)
test_miou, test_per_cat_miou = evaluate(net, test_loader, dev, (epoch + 1) % 5 ==0)
if test_miou > best_test_miou:
best_test_miou = test_miou
best_test_per_cat_miou = test_per_cat_miou
if args.save_model_path:
torch.save(net.state_dict(), args.save_model_path)
print('Current test mIoU: %.5f (best: %.5f), per-Category mIoU: %.5f (best: %.5f)' % (
test_miou, best_test_miou, test_per_cat_miou, best_test_per_cat_miou))
# Tensorboard
if args.tensorboard:
colored = paint(preds)
writer.add_mesh('data', vertices=data, colors=colored, global_step=epoch)
writer.add_scalar('training time for one epoch', training_time, global_step=epoch)
writer.add_scalar('AvgLoss', AvgLoss, global_step=epoch)
writer.add_scalar('AvgAcc', AvgAcc, global_step=epoch)
if (epoch + 1) % 5 == 0:
writer.add_scalar('test mIoU', test_miou, global_step=epoch)
writer.add_scalar('best test mIoU', best_test_miou, global_step=epoch)