"""
Author: Benny
Date: Nov 2019
"""
import argparse
import os
import torch
import datetime
import logging
import sys
import importlib
import shutil
import provider
import numpy as np
from pathlib import Path
from tqdm import tqdm
from dataset import PartNormalDataset
import hydra
import omegaconf
seg_classes = {'Earphone': [16, 17, 18], 'Motorbike': [30, 31, 32, 33, 34, 35], 'Rocket': [41, 42, 43],
'Car': [8, 9, 10, 11], 'Laptop': [28, 29], 'Cap': [6, 7], 'Skateboard': [44, 45, 46], 'Mug': [36, 37],
'Guitar': [19, 20, 21], 'Bag': [4, 5], 'Lamp': [24, 25, 26, 27], 'Table': [47, 48, 49],
'Airplane': [0, 1, 2, 3], 'Pistol': [38, 39, 40], 'Chair': [12, 13, 14, 15], 'Knife': [22, 23]}
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
def inplace_relu(m):
classname = m.__class__.__name__
if classname.find('ReLU') != -1:
m.inplace=True
def to_categorical(y, num_classes):
""" 1-hot encodes a tensor """
new_y = torch.eye(num_classes)[y.cpu().data.numpy(),]
if (y.is_cuda):
return new_y.cuda()
return new_y
@hydra.main(config_path='config', config_name='partseg')
def main(args):
omegaconf.OmegaConf.set_struct(args, False)
'''HYPER PARAMETER'''
os.environ["CUDA_VISIBLE_DEVICES"] = str(args.gpu)
# print('GPU available: {}'.format(torch.cuda.is_available()))
logger = logging.getLogger(__name__)
# print(args.pretty())
# use pretty print to print the config
root = hydra.utils.to_absolute_path('data/shapenetcore_partanno_segmentation_benchmark_v0_normal/')
TRAIN_DATASET = PartNormalDataset(root=root, npoints=args.num_point, split='trainval', normal_channel=args.normal)
trainDataLoader = torch.utils.data.DataLoader(TRAIN_DATASET, batch_size=args.batch_size, shuffle=True, num_workers=10, drop_last=True)
TEST_DATASET = PartNormalDataset(root=root, npoints=args.num_point, split='test', normal_channel=args.normal)
testDataLoader = torch.utils.data.DataLoader(TEST_DATASET, batch_size=args.batch_size, shuffle=False, num_workers=10)
'''MODEL LOADING'''
args.input_dim = (6 if args.normal else 3) + 16
args.num_class = 50
num_category = 16
num_part = args.num_class
shutil.copy(hydra.utils.to_absolute_path('models/{}/model.py'.format(args.model.name)), '.')
# print if gpu is available
logger.info('GPU available: {}'.format(torch.cuda.is_available()))
classifier = getattr(importlib.import_module('models.{}.model'.format(args.model.name)), 'PointTransformerSeg')(args).cuda()
criterion = torch.nn.CrossEntropyLoss()
try:
checkpoint = torch.load('best_model.pth')
start_epoch = checkpoint['epoch']
classifier.load_state_dict(checkpoint['model_state_dict'])
logger.info('Use pretrain model')
except:
logger.info('No existing model, starting training from scratch...')
start_epoch = 0
if args.optimizer == 'Adam':
optimizer = torch.optim.Adam(
classifier.parameters(),
lr=args.learning_rate,
betas=(0.9, 0.999),
eps=1e-08,
weight_decay=args.weight_decay
)
else:
optimizer = torch.optim.SGD(classifier.parameters(), lr=args.learning_rate, momentum=0.9)
def bn_momentum_adjust(m, momentum):
if isinstance(m, torch.nn.BatchNorm2d) or isinstance(m, torch.nn.BatchNorm1d):
m.momentum = momentum
LEARNING_RATE_CLIP = 1e-5
MOMENTUM_ORIGINAL = 0.1
MOMENTUM_DECCAY = 0.5
MOMENTUM_DECCAY_STEP = args.step_size
best_acc = 0
global_epoch = 0
best_class_avg_iou = 0
best_inctance_avg_iou = 0
for epoch in range(start_epoch, args.epoch):
mean_correct = []
logger.info('Epoch %d (%d/%s):' % (global_epoch + 1, epoch + 1, args.epoch))
'''Adjust learning rate and BN momentum'''
lr = max(args.learning_rate * (args.lr_decay ** (epoch // args.step_size)), LEARNING_RATE_CLIP)
logger.info('Learning rate:%f' % lr)
for param_group in optimizer.param_groups:
param_group['lr'] = lr
momentum = MOMENTUM_ORIGINAL * (MOMENTUM_DECCAY ** (epoch // MOMENTUM_DECCAY_STEP))
if momentum < 0.01:
momentum = 0.01
print('BN momentum updated to: %f' % momentum)
classifier = classifier.apply(lambda x: bn_momentum_adjust(x, momentum))
classifier = classifier.train()
'''learning one epoch'''
for i, (points, label, target) in tqdm(enumerate(trainDataLoader), total=len(trainDataLoader), smoothing=0.9):
points = points.data.numpy()
points[:, :, 0:3] = provider.random_scale_point_cloud(points[:, :, 0:3])
points[:, :, 0:3] = provider.shift_point_cloud(points[:, :, 0:3])
points = torch.Tensor(points)
points, label, target = points.float().cuda(), label.long().cuda(), target.long().cuda()
optimizer.zero_grad()
seg_pred = classifier(torch.cat([points, to_categorical(label, num_category).repeat(1, points.shape[1], 1)], -1))
seg_pred = seg_pred.contiguous().view(-1, num_part)
target = target.view(-1, 1)[:, 0]
pred_choice = seg_pred.data.max(1)[1]
correct = pred_choice.eq(target.data).cpu().sum()
mean_correct.append(correct.item() / (args.batch_size * args.num_point))
loss = criterion(seg_pred, target)
loss.backward()
optimizer.step()
train_instance_acc = np.mean(mean_correct)
logger.info('Train accuracy is: %.5f' % train_instance_acc)
with torch.no_grad():
test_metrics = {}
total_correct = 0
total_seen = 0
total_seen_class = [0 for _ in range(num_part)]
total_correct_class = [0 for _ in range(num_part)]
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
classifier = classifier.eval()
for batch_id, (points, label, target) in tqdm(enumerate(testDataLoader), total=len(testDataLoader), smoothing=0.9):
cur_batch_size, NUM_POINT, _ = points.size()
points, label, target = points.float().cuda(), label.long().cuda(), target.long().cuda()
seg_pred = classifier(torch.cat([points, to_categorical(label, num_category).repeat(1, points.shape[1], 1)], -1))
cur_pred_val = seg_pred.cpu().data.numpy()
cur_pred_val_logits = cur_pred_val
cur_pred_val = np.zeros((cur_batch_size, NUM_POINT)).astype(np.int32)
target = target.cpu().data.numpy()
for i in range(cur_batch_size):
cat = seg_label_to_cat[target[i, 0]]
logits = cur_pred_val_logits[i, :, :]
cur_pred_val[i, :] = np.argmax(logits[:, seg_classes[cat]], 1) + seg_classes[cat][0]
correct = np.sum(cur_pred_val == target)
total_correct += correct
total_seen += (cur_batch_size * NUM_POINT)
for l in range(num_part):
total_seen_class[l] += np.sum(target == l)
total_correct_class[l] += (np.sum((cur_pred_val == l) & (target == l)))
for i in range(cur_batch_size):
segp = cur_pred_val[i, :]
segl = target[i, :]
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 (np.sum(segl == l) == 0) and (
np.sum(segp == l) == 0): # part is not present, no prediction as well
part_ious[l - seg_classes[cat][0]] = 1.0
else:
part_ious[l - seg_classes[cat][0]] = np.sum((segl == l) & (segp == l)) / float(
np.sum((segl == l) | (segp == l)))
shape_ious[cat].append(np.mean(part_ious))
all_shape_ious = []
for cat in shape_ious.keys():
for iou in shape_ious[cat]:
all_shape_ious.append(iou)
shape_ious[cat] = np.mean(shape_ious[cat])
mean_shape_ious = np.mean(list(shape_ious.values()))
test_metrics['accuracy'] = total_correct / float(total_seen)
test_metrics['class_avg_accuracy'] = np.mean(
np.array(total_correct_class) / np.array(total_seen_class, dtype=np.float32))
for cat in sorted(shape_ious.keys()):
logger.info('eval mIoU of %s %f' % (cat + ' ' * (14 - len(cat)), shape_ious[cat]))
test_metrics['class_avg_iou'] = mean_shape_ious
test_metrics['inctance_avg_iou'] = np.mean(all_shape_ious)
logger.info('Epoch %d test Accuracy: %f Class avg mIOU: %f Inctance avg mIOU: %f' % (
epoch + 1, test_metrics['accuracy'], test_metrics['class_avg_iou'], test_metrics['inctance_avg_iou']))
if (test_metrics['inctance_avg_iou'] >= best_inctance_avg_iou):
logger.info('Save model...')
savepath = 'best_model.pth'
logger.info('Saving at %s' % savepath)
state = {
'epoch': epoch,
'train_acc': train_instance_acc,
'test_acc': test_metrics['accuracy'],
'class_avg_iou': test_metrics['class_avg_iou'],
'inctance_avg_iou': test_metrics['inctance_avg_iou'],
'model_state_dict': classifier.state_dict(),
'optimizer_state_dict': optimizer.state_dict(),
}
torch.save(state, savepath)
logger.info('Saving model....')
if test_metrics['accuracy'] > best_acc:
best_acc = test_metrics['accuracy']
if test_metrics['class_avg_iou'] > best_class_avg_iou:
best_class_avg_iou = test_metrics['class_avg_iou']
if test_metrics['inctance_avg_iou'] > best_inctance_avg_iou:
best_inctance_avg_iou = test_metrics['inctance_avg_iou']
logger.info('Best accuracy is: %.5f' % best_acc)
logger.info('Best class avg mIOU is: %.5f' % best_class_avg_iou)
logger.info('Best inctance avg mIOU is: %.5f' % best_inctance_avg_iou)
global_epoch += 1
if __name__ == '__main__':
main()