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main_semseg.py
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#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
@Author: An Tao, Pengliang Ji
@Contact: [email protected], [email protected]
@File: main_semseg.py
@Time: 2021/7/20 7:49 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, StepLR
from data import S3DIS
from model import DGCNN_semseg
import numpy as np
from torch.utils.data import DataLoader
from util import cal_loss, IOStream
import sklearn.metrics as metrics
from plyfile import PlyData, PlyElement
global room_seg
room_seg = []
global room_pred
room_pred = []
global visual_warning
visual_warning = True
def _init_():
if not os.path.exists('outputs'):
os.makedirs('outputs')
if not os.path.exists('outputs/'+args.exp_name):
os.makedirs('outputs/'+args.exp_name)
if not os.path.exists('outputs/'+args.exp_name+'/'+'models'):
os.makedirs('outputs/'+args.exp_name+'/'+'models')
os.system('cp main_semseg.py outputs'+'/'+args.exp_name+'/'+'main_semseg.py.backup')
os.system('cp model.py outputs' + '/' + args.exp_name + '/' + 'model.py.backup')
os.system('cp util.py outputs' + '/' + args.exp_name + '/' + 'util.py.backup')
os.system('cp data.py outputs' + '/' + args.exp_name + '/' + 'data.py.backup')
def calculate_sem_IoU(pred_np, seg_np, visual=False):
I_all = np.zeros(13)
U_all = np.zeros(13)
for sem_idx in range(seg_np.shape[0]):
for sem in range(13):
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
if visual:
for sem in range(13):
if U_all[sem] == 0:
I_all[sem] = 1
U_all[sem] = 1
return I_all / U_all
def visualization(visu, visu_format, test_choice, data, seg, pred, visual_file_index, semseg_colors):
global room_seg, room_pred
global visual_warning
visu = visu.split('_')
for i in range(0, data.shape[0]):
RGB = []
RGB_gt = []
skip = False
with open("data/indoor3d_sem_seg_hdf5_data_test/room_filelist.txt") as f:
files = f.readlines()
test_area = files[visual_file_index][5]
roomname = files[visual_file_index][7:-1]
if visual_file_index + 1 < len(files):
roomname_next = files[visual_file_index+1][7:-1]
else:
roomname_next = ''
if visu[0] != 'all':
if len(visu) == 2:
if visu[0] != 'area' or visu[1] != test_area:
skip = True
else:
visual_warning = False
elif len(visu) == 4:
if visu[0] != 'area' or visu[1] != test_area or visu[2] != roomname.split('_')[0] or visu[3] != roomname.split('_')[1]:
skip = True
else:
visual_warning = False
else:
skip = True
elif test_choice !='all':
skip = True
else:
visual_warning = False
if skip:
visual_file_index = visual_file_index + 1
else:
if not os.path.exists('outputs/'+args.exp_name+'/'+'visualization'+'/'+'area_'+test_area+'/'+roomname):
os.makedirs('outputs/'+args.exp_name+'/'+'visualization'+'/'+'area_'+test_area+'/'+roomname)
data = np.loadtxt('data/indoor3d_sem_seg_hdf5_data_test/raw_data3d/Area_'+test_area+'/'+roomname+'('+str(visual_file_index)+').txt')
visual_file_index = visual_file_index + 1
for j in range(0, data.shape[0]):
RGB.append(semseg_colors[int(pred[i][j])])
RGB_gt.append(semseg_colors[int(seg[i][j])])
data = data[:,[1,2,0]]
xyzRGB = np.concatenate((data, np.array(RGB)), axis=1)
xyzRGB_gt = np.concatenate((data, np.array(RGB_gt)), axis=1)
room_seg.append(seg[i].cpu().numpy())
room_pred.append(pred[i].cpu().numpy())
f = open('outputs/'+args.exp_name+'/'+'visualization'+'/'+'area_'+test_area+'/'+roomname+'/'+roomname+'.txt', "a")
f_gt = open('outputs/'+args.exp_name+'/'+'visualization'+'/'+'area_'+test_area+'/'+roomname+'/'+roomname+'_gt.txt', "a")
np.savetxt(f, xyzRGB, fmt='%s', delimiter=' ')
np.savetxt(f_gt, xyzRGB_gt, fmt='%s', delimiter=' ')
if roomname != roomname_next:
mIoU = np.mean(calculate_sem_IoU(np.array(room_pred), np.array(room_seg), visual=True))
mIoU = str(round(mIoU, 4))
room_pred = []
room_seg = []
if visu_format == 'ply':
filepath = 'outputs/'+args.exp_name+'/'+'visualization'+'/'+'area_'+test_area+'/'+roomname+'/'+roomname+'_pred_'+mIoU+'.ply'
filepath_gt = 'outputs/'+args.exp_name+'/'+'visualization'+'/'+'area_'+test_area+'/'+roomname+'/'+roomname+'_gt.ply'
xyzRGB = np.loadtxt('outputs/'+args.exp_name+'/'+'visualization'+'/'+'area_'+test_area+'/'+roomname+'/'+roomname+'.txt')
xyzRGB_gt = np.loadtxt('outputs/'+args.exp_name+'/'+'visualization'+'/'+'area_'+test_area+'/'+roomname+'/'+roomname+'_gt.txt')
xyzRGB = [(xyzRGB[i, 0], xyzRGB[i, 1], xyzRGB[i, 2], xyzRGB[i, 3], xyzRGB[i, 4], xyzRGB[i, 5]) for i in range(xyzRGB.shape[0])]
xyzRGB_gt = [(xyzRGB_gt[i, 0], xyzRGB_gt[i, 1], xyzRGB_gt[i, 2], xyzRGB_gt[i, 3], xyzRGB_gt[i, 4], xyzRGB_gt[i, 5]) for i in range(xyzRGB_gt.shape[0])]
vertex = PlyElement.describe(np.array(xyzRGB, dtype=[('x', 'f4'), ('y', 'f4'), ('z', 'f4'), ('red', 'u1'), ('green', 'u1'), ('blue', 'u1')]), 'vertex')
PlyData([vertex]).write(filepath)
print('PLY visualization file saved in', filepath)
vertex = PlyElement.describe(np.array(xyzRGB_gt, dtype=[('x', 'f4'), ('y', 'f4'), ('z', 'f4'), ('red', 'u1'), ('green', 'u1'), ('blue', 'u1')]), 'vertex')
PlyData([vertex]).write(filepath_gt)
print('PLY visualization file saved in', filepath_gt)
os.system('rm -rf '+'outputs/'+args.exp_name+'/visualization/area_'+test_area+'/'+roomname+'/*.txt')
else:
filename = 'outputs/'+args.exp_name+'/'+'visualization'+'/'+'area_'+test_area+'/'+roomname+'/'+roomname+'.txt'
filename_gt = 'outputs/'+args.exp_name+'/'+'visualization'+'/'+'area_'+test_area+'/'+roomname+'/'+roomname+'_gt.txt'
filename_mIoU = 'outputs/'+args.exp_name+'/'+'visualization'+'/'+'area_'+test_area+'/'+roomname+'/'+roomname+'_pred_'+mIoU+'.txt'
os.rename(filename, filename_mIoU)
print('TXT visualization file saved in', filename_mIoU)
print('TXT visualization file saved in', filename_gt)
elif visu_format != 'ply' and visu_format != 'txt':
print('ERROR!! Unknown visualization format: %s, please use txt or ply.' % \
(visu_format))
exit()
def train(args, io):
train_loader = DataLoader(S3DIS(partition='train', num_points=args.num_points, test_area=args.test_area),
num_workers=8, batch_size=args.batch_size, shuffle=True, drop_last=True)
test_loader = DataLoader(S3DIS(partition='test', num_points=args.num_points, test_area=args.test_area),
num_workers=8, batch_size=args.test_batch_size, shuffle=True, drop_last=False)
device = torch.device("cuda" if args.cuda else "cpu")
#Try to load models
if args.model == 'dgcnn':
model = DGCNN_semseg(args).to(device)
else:
raise Exception("Not implemented")
print(str(model))
model = nn.DataParallel(model)
print("Let's use", torch.cuda.device_count(), "GPUs!")
if args.use_sgd:
print("Use SGD")
opt = optim.SGD(model.parameters(), lr=args.lr*100, momentum=args.momentum, weight_decay=1e-4)
else:
print("Use Adam")
opt = optim.Adam(model.parameters(), lr=args.lr, weight_decay=1e-4)
if args.scheduler == 'cos':
scheduler = CosineAnnealingLR(opt, args.epochs, eta_min=1e-3)
elif args.scheduler == 'step':
scheduler = StepLR(opt, 20, 0.5, args.epochs)
criterion = cal_loss
best_test_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 = []
train_label_seg = []
for data, seg in train_loader:
data, seg = data.to(device), seg.to(device)
data = data.permute(0, 2, 1)
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, 13), seg.view(-1,1).squeeze())
loss.backward()
opt.step()
pred = seg_pred.max(dim=2)[1] # (batch_size, num_points)
count += batch_size
train_loss += loss.item() * batch_size
seg_np = seg.cpu().numpy() # (batch_size, num_points)
pred_np = pred.detach().cpu().numpy() # (batch_size, num_points)
train_true_cls.append(seg_np.reshape(-1)) # (batch_size * num_points)
train_pred_cls.append(pred_np.reshape(-1)) # (batch_size * num_points)
train_true_seg.append(seg_np)
train_pred_seg.append(pred_np)
if args.scheduler == 'cos':
scheduler.step()
elif args.scheduler == 'step':
if opt.param_groups[0]['lr'] > 1e-5:
scheduler.step()
if opt.param_groups[0]['lr'] < 1e-5:
for param_group in opt.param_groups:
param_group['lr'] = 1e-5
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)
outstr = 'Train %d, loss: %.6f, train acc: %.6f, train avg acc: %.6f, train iou: %.6f' % (epoch,
train_loss*1.0/count,
train_acc,
avg_per_class_acc,
np.mean(train_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)
data = data.permute(0, 2, 1)
batch_size = data.size()[0]
seg_pred = model(data)
seg_pred = seg_pred.permute(0, 2, 1).contiguous()
loss = criterion(seg_pred.view(-1, 13), seg.view(-1,1).squeeze())
pred = seg_pred.max(dim=2)[1]
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)
outstr = 'Test %d, loss: %.6f, test acc: %.6f, test avg acc: %.6f, test iou: %.6f' % (epoch,
test_loss*1.0/count,
test_acc,
avg_per_class_acc,
np.mean(test_ious))
io.cprint(outstr)
if np.mean(test_ious) >= best_test_iou:
best_test_iou = np.mean(test_ious)
torch.save(model.state_dict(), 'outputs/%s/models/model_%s.t7' % (args.exp_name, args.test_area))
def test(args, io):
all_true_cls = []
all_pred_cls = []
all_true_seg = []
all_pred_seg = []
for test_area in range(1,7):
visual_file_index = 0
test_area = str(test_area)
if os.path.exists("data/indoor3d_sem_seg_hdf5_data_test/room_filelist.txt"):
with open("data/indoor3d_sem_seg_hdf5_data_test/room_filelist.txt") as f:
for line in f:
if (line[5]) == test_area:
break
visual_file_index = visual_file_index + 1
if (args.test_area == 'all') or (test_area == args.test_area):
test_loader = DataLoader(S3DIS(partition='test', num_points=args.num_points, test_area=test_area),
batch_size=args.test_batch_size, shuffle=False, drop_last=False)
device = torch.device("cuda" if args.cuda else "cpu")
#Try to load models
semseg_colors = test_loader.dataset.semseg_colors
if args.model == 'dgcnn':
model = DGCNN_semseg(args).to(device)
else:
raise Exception("Not implemented")
model = nn.DataParallel(model)
model.load_state_dict(torch.load(os.path.join(args.model_root, 'model_%s.t7' % test_area)))
model = model.eval()
test_acc = 0.0
count = 0.0
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)
data = data.permute(0, 2, 1)
batch_size = data.size()[0]
seg_pred = model(data)
seg_pred = seg_pred.permute(0, 2, 1).contiguous()
pred = seg_pred.max(dim=2)[1]
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)
# visiualization
visualization(args.visu, args.visu_format, args.test_area, data, seg, pred, visual_file_index, semseg_colors)
visual_file_index = visual_file_index + data.shape[0]
if visual_warning and args.visu != '':
print('Visualization Failed: You can only choose a room to visualize within the scope of the test area')
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)
outstr = 'Test :: test area: %s, test acc: %.6f, test avg acc: %.6f, test iou: %.6f' % (test_area,
test_acc,
avg_per_class_acc,
np.mean(test_ious))
io.cprint(outstr)
all_true_cls.append(test_true_cls)
all_pred_cls.append(test_pred_cls)
all_true_seg.append(test_true_seg)
all_pred_seg.append(test_pred_seg)
if args.test_area == 'all':
all_true_cls = np.concatenate(all_true_cls)
all_pred_cls = np.concatenate(all_pred_cls)
all_acc = metrics.accuracy_score(all_true_cls, all_pred_cls)
avg_per_class_acc = metrics.balanced_accuracy_score(all_true_cls, all_pred_cls)
all_true_seg = np.concatenate(all_true_seg, axis=0)
all_pred_seg = np.concatenate(all_pred_seg, axis=0)
all_ious = calculate_sem_IoU(all_pred_seg, all_true_seg)
outstr = 'Overall Test :: test acc: %.6f, test avg acc: %.6f, test iou: %.6f' % (all_acc,
avg_per_class_acc,
np.mean(all_ious))
io.cprint(outstr)
if __name__ == "__main__":
# Training settings
parser = argparse.ArgumentParser(description='Point Cloud Part Segmentation')
parser.add_argument('--exp_name', type=str, default='exp', metavar='N',
help='Name of the experiment')
parser.add_argument('--model', type=str, default='dgcnn', metavar='N',
choices=['dgcnn'],
help='Model to use, [dgcnn]')
parser.add_argument('--dataset', type=str, default='S3DIS', metavar='N',
choices=['S3DIS'])
parser.add_argument('--test_area', type=str, default=None, metavar='N',
choices=['1', '2', '3', '4', '5', '6', 'all'])
parser.add_argument('--batch_size', type=int, default=32, 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=100, metavar='N',
help='number of episode to train ')
parser.add_argument('--use_sgd', type=bool, default=True,
help='Use SGD')
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('--momentum', type=float, default=0.9, metavar='M',
help='SGD momentum (default: 0.9)')
parser.add_argument('--scheduler', type=str, default='cos', metavar='N',
choices=['cos', 'step'],
help='Scheduler to use, [cos, step]')
parser.add_argument('--no_cuda', type=bool, default=False,
help='enables CUDA training')
parser.add_argument('--seed', type=int, default=1, metavar='S',
help='random seed (default: 1)')
parser.add_argument('--eval', type=bool, default=False,
help='evaluate the model')
parser.add_argument('--num_points', type=int, default=4096,
help='num of points to use')
parser.add_argument('--dropout', type=float, default=0.5,
help='dropout rate')
parser.add_argument('--emb_dims', type=int, default=1024, metavar='N',
help='Dimension of embeddings')
parser.add_argument('--k', type=int, default=20, metavar='N',
help='Num of nearest neighbors to use')
parser.add_argument('--model_root', type=str, default='', metavar='N',
help='Pretrained model root')
parser.add_argument('--visu', type=str, default='',
help='visualize the model')
parser.add_argument('--visu_format', type=str, default='ply',
help='file format of visualization')
args = parser.parse_args()
_init_()
io = IOStream('outputs/' + args.exp_name + '/run.log')
io.cprint(str(args))
args.cuda = not args.no_cuda and torch.cuda.is_available()
torch.manual_seed(args.seed)
if args.cuda:
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)