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main_seg.py
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main_seg.py
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"""
@Author: Yue Wang
@Contact: yuewangx@mit.edu
@File: main_seg.py
@Time: 2018/10/13 10:39 PM
Modified by
@Author: Manxi Lin
@Contact: manli@dtu.dk
@Time: 2022/07/10 16:45 PM
"""
from __future__ import print_function
import os
import argparse
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.optim.lr_scheduler import CosineAnnealingLR
from data_utils import Toronto3D
from models.diffConv_seg import Model
import numpy as np
from torch.utils.data import DataLoader
from misc import cal_loss, IOStream
import sklearn.metrics as metrics
def calculate_sem_IoU(pred_np, seg_np):
I_all = np.zeros(9)
U_all = np.zeros(9)
for sem_idx in range(seg_np.shape[0]):
for sem in range(9):
I = np.sum(np.logical_and(pred_np[sem_idx] == sem, seg_np[sem_idx] == sem))
U = np.sum(np.logical_or(pred_np[sem_idx] == sem, seg_np[sem_idx] == sem))
I_all[sem] += I
U_all[sem] += U
# 0 is unlabelled, thus is not considered
I_all = I_all[1:]
U_all = U_all[1:]
return I_all / U_all
def calculate_class_IoU(pred_np, seg_np):
'''
return iou for each category
'''
IOU = np.zeros((seg_np.shape[0], 9))
for sem_idx in range(seg_np.shape[0]):
n = 9
for sem in range(9):
I = np.sum(np.logical_and(pred_np[sem_idx] == sem, seg_np[sem_idx] == sem))
U = np.sum(np.logical_or(pred_np[sem_idx] == sem, seg_np[sem_idx] == sem))
if U == 0:
I = 1
U = 1
n -= 1
IOU[sem_idx, sem] = I / U
return IOU[:,1:] # remove the unlabelled class (0)
def train(args, io):
train_loader = DataLoader(Toronto3D(partition='train'),
num_workers=16, batch_size=args.batch_size, shuffle=True, drop_last=True)
test_loader = DataLoader(Toronto3D(partition='test'),
num_workers=16, batch_size=args.test_batch_size, shuffle=False, drop_last=False)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
#Try to load models
model = Model(args, num_classes=9)
model.to(device)
model = nn.DataParallel(model)
print("Let's use", torch.cuda.device_count(), "GPUs!")
opt = optim.AdamW(model.parameters(), lr=args.lr, weight_decay=1e-4)
scheduler = CosineAnnealingLR(opt, args.epochs, eta_min=1e-4)
criterion = cal_loss
best_mean_iou = 0
for epoch in range(args.epochs):
####################
# Train
####################
train_loss = 0.0
count = 0.0
model.train()
train_true_cls = []
train_pred_cls = []
train_true_seg = []
train_pred_seg = []
for data, seg in train_loader:
data, seg = data.to(device), seg.to(device)
batch_size = data.size()[0]
opt.zero_grad()
seg_pred = model(data)
seg_pred = seg_pred.permute(0, 2, 1).contiguous()
loss = criterion(seg_pred.view(-1, 9), seg.view(-1,1).squeeze())
loss.backward()
opt.step()
pred = seg_pred.argmax(dim=2)
count += batch_size
train_loss += loss.item() * batch_size
seg_np = seg.cpu().numpy()
pred_np = pred.detach().cpu().numpy()
train_true_cls.append(seg_np.reshape(-1))
train_pred_cls.append(pred_np.reshape(-1))
train_true_seg.append(seg_np)
train_pred_seg.append(pred_np)
scheduler.step()
train_true_cls = np.concatenate(train_true_cls)
train_pred_cls = np.concatenate(train_pred_cls)
train_acc = metrics.accuracy_score(train_true_cls, train_pred_cls)
avg_per_class_acc = metrics.balanced_accuracy_score(train_true_cls, train_pred_cls)
train_true_seg = np.concatenate(train_true_seg, axis=0)
train_pred_seg = np.concatenate(train_pred_seg, axis=0)
train_ious = calculate_sem_IoU(train_pred_seg, train_true_seg)
ious = calculate_class_IoU(train_pred_seg, train_true_seg)
outstr = 'Train %d, loss: %.6f, train acc: %.6f, train avg acc: %.6f, train iou: %.6f, mean iou: %.6f' % (epoch,
train_loss*1.0/count,
train_acc,
avg_per_class_acc,
np.mean(train_ious),
np.mean(ious))
io.cprint(outstr)
####################
# Test
####################
test_loss = 0.0
count = 0.0
model.eval()
test_true_cls = []
test_pred_cls = []
test_true_seg = []
test_pred_seg = []
for data, seg in test_loader:
data, seg = data.to(device), seg.to(device)
batch_size = data.size()[0]
with torch.no_grad():
seg_pred = model(data)
seg_pred = seg_pred.permute(0, 2, 1).contiguous()
loss = criterion(seg_pred.view(-1, 9), seg.view(-1,1).squeeze())
pred = seg_pred.argmax(dim=2)
count += batch_size
test_loss += loss.item() * batch_size
seg_np = seg.cpu().numpy()
pred_np = pred.detach().cpu().numpy()
test_true_cls.append(seg_np.reshape(-1))
test_pred_cls.append(pred_np.reshape(-1))
test_true_seg.append(seg_np)
test_pred_seg.append(pred_np)
test_true_cls = np.concatenate(test_true_cls)
test_pred_cls = np.concatenate(test_pred_cls)
test_acc = metrics.accuracy_score(test_true_cls, test_pred_cls)
avg_per_class_acc = metrics.balanced_accuracy_score(test_true_cls, test_pred_cls)
test_true_seg = np.concatenate(test_true_seg, axis=0)
test_pred_seg = np.concatenate(test_pred_seg, axis=0)
test_ious = calculate_sem_IoU(test_pred_seg, test_true_seg)
ious = calculate_class_IoU(test_pred_seg, test_true_seg)
outstr = 'Test %d, loss: %.6f, test acc: %.6f, test avg acc: %.6f, test iou: %.6f, mean iou: %.6f' % (epoch,
test_loss*1.0/count,
test_acc,
avg_per_class_acc,
np.mean(test_ious),
np.mean(ious))
io.cprint(outstr)
if np.mean(ious) >= best_mean_iou:
best_mean_iou = np.mean(ious)
torch.save(model.state_dict(), 'checkpoints/%s.pth' %args.exp_name)
def test(args, io):
test_loader = DataLoader(Toronto3D(partition='test'),
num_workers=16, batch_size=args.test_batch_size, shuffle=False, drop_last=False)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
#Try to load models
model = Model(args, num_classes=9)
model.to(device)
model = nn.DataParallel(model)
print("Let's use", torch.cuda.device_count(), "GPUs!")
print('load model')
model.load_state_dict(torch.load(args.model_path))
with torch.no_grad():
####################
# Test
####################
count = 0.0
model.eval()
test_true_cls = []
test_pred_cls = []
test_true_seg = []
test_pred_seg = []
for data, seg in test_loader:
data, seg = data.to(device), seg.to(device)
batch_size = data.size()[0]
with torch.no_grad():
seg_pred = model(data)
seg_pred = seg_pred.permute(0, 2, 1).contiguous()
pred = seg_pred.max(dim=2)[1]
count += batch_size
seg_np = seg.cpu().numpy()
pred_np = pred.detach().cpu().numpy()
test_true_cls.append(seg_np.reshape(-1))
test_pred_cls.append(pred_np.reshape(-1))
test_true_seg.append(seg_np)
test_pred_seg.append(pred_np)
test_true_cls = np.concatenate(test_true_cls)
test_pred_cls = np.concatenate(test_pred_cls)
test_acc = metrics.accuracy_score(test_true_cls, test_pred_cls)
avg_per_class_acc = metrics.balanced_accuracy_score(test_true_cls, test_pred_cls)
test_true_seg = np.concatenate(test_true_seg, axis=0)
test_pred_seg = np.concatenate(test_pred_seg, axis=0)
test_ious = calculate_sem_IoU(test_pred_seg, test_true_seg)
ious = calculate_class_IoU(test_pred_seg, test_true_seg)
ious = np.mean(ious, axis=0)
category_names = ['Road', 'Road Mark', 'Natural', 'Building', 'Util. line', 'Pole', 'Car', 'Fence']
io.cprint('Category-wise iou:')
for c, iou in zip(category_names, ious):
io.cprint('%s: %.4f'%(c, iou))
outstr = 'Overall Test :: test acc: %.4f, test avg acc: %.4f, test iou: %.4f, mean iou: %.4f' % (test_acc,
avg_per_class_acc,
np.mean(test_ious),
np.mean(ious))
io.cprint(outstr)
if __name__ == "__main__":
# Training settings
parser = argparse.ArgumentParser(description='Point Cloud Scene Segmentation')
parser.add_argument('--exp_name', type=str, default='exp', metavar='N',
help='Name of the experiment')
parser.add_argument('--batch_size', type=int, default=16, metavar='batch_size',
help='Size of batch)')
parser.add_argument('--test_batch_size', type=int, default=16, metavar='batch_size',
help='Size of batch)')
parser.add_argument('--epochs', type=int, default=2000, metavar='N',
help='number of episode to train ')
parser.add_argument('--lr', type=float, default=0.001, metavar='LR',
help='learning rate (default: 0.001, 0.1 if using sgd)')
parser.add_argument('--seed', type=int, default=42, metavar='S',
help='random seed (default: 42)')
parser.add_argument('--eval', type=bool, default=False,
help='evaluate the model')
parser.add_argument('--num_points', type=int, default=2048,
help='num of points to use')
parser.add_argument('--dropout', type=float, default=0.5,
help='dropout rate')
parser.add_argument('--radius', type=float, default=0.005,
help='searching radius')
parser.add_argument('--model_path', type=str, default='', metavar='N',
help='Pretrained model path')
args = parser.parse_args()
io = IOStream(os.path.join('./logs', args.exp_name))
io.cprint(str(args))
torch.manual_seed(args.seed)
if torch.cuda.is_available():
io.cprint(
'Using GPU : ' + str(torch.cuda.current_device()) + ' from ' + str(torch.cuda.device_count()) + ' devices')
torch.cuda.manual_seed(args.seed)
else:
io.cprint('Using CPU')
if not args.eval:
train(args, io)
else:
test(args, io)