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- """
- 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
- import torch.optim as optim
- from timm.scheduler import CosineLRScheduler
- from pathlib import Path
- from tqdm import tqdm
- from dataset import PartNormalDataset
-
- BASE_DIR = os.path.dirname(os.path.abspath(__file__))
- ROOT_DIR = BASE_DIR
- sys.path.append(os.path.join(ROOT_DIR, 'models'))
-
- 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
-
-
- def parse_args():
- parser = argparse.ArgumentParser('Model')
- parser.add_argument('--model', type=str, default='pt', help='model name')
- parser.add_argument('--batch_size', type=int, default=16, help='batch Size during training')
- parser.add_argument('--epoch', default=300, type=int, help='epoch to run')
- parser.add_argument('--warmup_epoch', default=10, type=int, help='warmup epoch')
- parser.add_argument('--learning_rate', default=0.0002, type=float, help='initial learning rate')
- parser.add_argument('--gpu', type=str, default='0', help='specify GPU devices')
- # parser.add_argument('--optimizer', type=str, default='AdamW', help='Adam or SGD')
- parser.add_argument('--log_dir', type=str, default='./exp', help='log path')
- # parser.add_argument('--decay_rate', type=float, default=1e-4, help='weight decay')
- parser.add_argument('--npoint', type=int, default=2048, help='point Number')
- parser.add_argument('--normal', action='store_true', default=False, help='use normals')
- # parser.add_argument('--step_size', type=int, default=20, help='decay step for lr decay')
- # parser.add_argument('--lr_decay', type=float, default=0.5, help='decay rate for lr decay')
- parser.add_argument('--ckpts', type=str, default='../best/pretrain/m0.6R_1_pretrain300.pth', help='ckpts')
- parser.add_argument('--root', type=str, default='../data/shapenetcore_partanno_segmentation_benchmark_v0_normal/', help='data root')
- return parser.parse_args()
-
-
- def main(args):
- def log_string(str):
- logger.info(str)
- print(str)
-
- '''HYPER PARAMETER'''
- os.environ["CUDA_VISIBLE_DEVICES"] = args.gpu
-
- '''CREATE DIR'''
- timestr = str(datetime.datetime.now().strftime('%Y-%m-%d_%H-%M'))
- exp_dir = Path('./log/')
- exp_dir.mkdir(exist_ok=True)
- exp_dir = exp_dir.joinpath('part_seg')
- exp_dir.mkdir(exist_ok=True)
- if args.log_dir is None:
- exp_dir = exp_dir.joinpath(timestr)
- else:
- exp_dir = exp_dir.joinpath(args.log_dir)
- exp_dir.mkdir(exist_ok=True)
- checkpoints_dir = exp_dir.joinpath('checkpoints/')
- checkpoints_dir.mkdir(exist_ok=True)
- log_dir = exp_dir.joinpath('logs/')
- log_dir.mkdir(exist_ok=True)
-
- '''LOG'''
- args = parse_args()
- logger = logging.getLogger("Model")
- logger.setLevel(logging.INFO)
- formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
- file_handler = logging.FileHandler('%s/%s.txt' % (log_dir, args.model))
- file_handler.setLevel(logging.INFO)
- file_handler.setFormatter(formatter)
- logger.addHandler(file_handler)
- log_string('PARAMETER ...')
- log_string(args)
-
- root = args.root
-
- TRAIN_DATASET = PartNormalDataset(root=root, npoints=args.npoint, 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.npoint, split='test', normal_channel=args.normal)
- testDataLoader = torch.utils.data.DataLoader(TEST_DATASET, batch_size=args.batch_size, shuffle=False, num_workers=10)
- log_string("The number of training data is: %d" % len(TRAIN_DATASET))
- log_string("The number of test data is: %d" % len(TEST_DATASET))
-
- num_classes = 16
- num_part = 50
-
- '''MODEL LOADING'''
- MODEL = importlib.import_module(args.model)
- shutil.copy('models/%s.py' % args.model, str(exp_dir))
- # shutil.copy('models/pointnet2_utils.py', str(exp_dir))
-
- classifier = MODEL.get_model(num_part).cuda()
- criterion = MODEL.get_loss().cuda()
- classifier.apply(inplace_relu)
- print('# generator parameters:', sum(param.numel() for param in classifier.parameters()))
- start_epoch = 0
-
- if args.ckpts is not None:
- classifier.load_model_from_ckpt(args.ckpts)
-
- ## we use adamw and cosine scheduler
- def add_weight_decay(model, weight_decay=1e-5, skip_list=()):
- decay = []
- no_decay = []
- for name, param in model.named_parameters():
- if not param.requires_grad:
- continue # frozen weights
- if len(param.shape) == 1 or name.endswith(".bias") or 'token' in name or name in skip_list:
- # print(name)
- no_decay.append(param)
- else:
- decay.append(param)
- return [
- {'params': no_decay, 'weight_decay': 0.},
- {'params': decay, 'weight_decay': weight_decay}]
-
- param_groups = add_weight_decay(classifier, weight_decay=0.05)
- optimizer = optim.AdamW(param_groups, lr= args.learning_rate, weight_decay=0.05 )
-
- scheduler = CosineLRScheduler(optimizer,
- t_initial=args.epoch,
- t_mul=1,
- lr_min=1e-6,
- decay_rate=0.1,
- warmup_lr_init=1e-6,
- warmup_t=args.warmup_epoch,
- cycle_limit=1,
- t_in_epochs=True)
-
- best_acc = 0
- global_epoch = 0
- best_class_avg_iou = 0
- best_inctance_avg_iou = 0
-
- classifier.zero_grad()
- for epoch in range(start_epoch, args.epoch):
- mean_correct = []
-
- log_string('Epoch %d (%d/%s):' % (global_epoch + 1, epoch + 1, args.epoch))
- '''Adjust learning rate and BN momentum'''
-
- classifier = classifier.train()
- loss_batch = []
- num_iter = 0
- '''learning one epoch'''
- for i, (points, label, target) in tqdm(enumerate(trainDataLoader), total=len(trainDataLoader), smoothing=0.9):
- num_iter += 1
- 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()
- points = points.transpose(2, 1)
-
- seg_pred = classifier(points, to_categorical(label, num_classes))
- 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.npoint))
- loss = criterion(seg_pred, target)
- loss.backward()
- optimizer.step()
- loss_batch.append(loss.detach().cpu())
-
- if num_iter == 1:
-
- torch.nn.utils.clip_grad_norm_(classifier.parameters(), 10, norm_type=2)
- num_iter = 0
- optimizer.step()
- classifier.zero_grad()
-
- if isinstance(scheduler, list):
- for item in scheduler:
- item.step(epoch)
- else:
- scheduler.step(epoch)
-
- train_instance_acc = np.mean(mean_correct)
- loss1 = np.mean(loss_batch)
- log_string('Train accuracy is: %.5f' % train_instance_acc)
- log_string('Train loss: %.5f' % loss1)
- log_string('lr: %.6f' % optimizer.param_groups[0]['lr'])
-
- 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()
- points = points.transpose(2, 1)
- seg_pred = classifier(points, to_categorical(label, num_classes))
- 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.float))
- for cat in sorted(shape_ious.keys()):
- log_string('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)
-
- log_string('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 = str(checkpoints_dir) + '/best_model.pth'
- log_string('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)
- log_string('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']
- log_string('Best accuracy is: %.5f' % best_acc)
- log_string('Best class avg mIOU is: %.5f' % best_class_avg_iou)
- log_string('Best inctance avg mIOU is: %.5f' % best_inctance_avg_iou)
- global_epoch += 1
-
-
- if __name__ == '__main__':
- args = parse_args()
- main(args)
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