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main_cls.py
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main_cls.py
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from __future__ import print_function
import os
import argparse
import torch
import torch.nn as nn
import torch.optim as optim
from torch.optim.lr_scheduler import CosineAnnealingLR
from data import ModelNet40, ScanObjectNN, ScanObjectNN_hardest
from model import MAE3D, MAE3D_cls
import numpy as np
from torch.utils.data import DataLoader
from util import cal_loss, IOStream
import sklearn.metrics as metrics
from tqdm import tqdm
import time
def _init_():
if not os.path.exists('checkpoints'):
os.makedirs('checkpoints')
if not os.path.exists('checkpoints/' + file_name):
os.makedirs('checkpoints/' + file_name)
if not os.path.exists('checkpoints/' + file_name + '/' + 'models'):
os.makedirs('checkpoints/' + file_name + '/' + 'models')
os.system('cp main_cls.py checkpoints' + '/' + file_name + '/' + 'main_cls.py.backup')
os.system('cp model.py checkpoints' + '/' + file_name + '/' + 'model.py.backup')
os.system('cp util.py checkpoints' + '/' + file_name + '/' + 'util.py.backup')
os.system('cp data.py checkpoints' + '/' + file_name + '/' + 'data.py.backup')
def train(args, io):
if args.dataset == 'modelnet40':
train_loader = DataLoader(
ModelNet40(limited_ratio=args.limited_ratio, partition='train', num_points=args.num_points), num_workers=0,
batch_size=args.batch_size, shuffle=True, drop_last=True)
test_loader = DataLoader(ModelNet40(partition='test', num_points=args.num_points), num_workers=8,
batch_size=args.test_batch_size, shuffle=True, drop_last=False)
elif args.dataset == 'ScanObjectNN_objectonly':
train_loader = DataLoader(
ScanObjectNN(root='data/ScanObjectNN/main_split_nobg', partition='train', num_points=args.num_points), num_workers=8,
batch_size=args.batch_size, shuffle=True, drop_last=True)
test_loader = DataLoader(ScanObjectNN(root='data/ScanObjectNN/main_split_nobg', partition='test', num_points=args.num_points), num_workers=8,
batch_size=args.test_batch_size, shuffle=False, drop_last=False)
elif args.dataset == 'ScanObjectNN_objectbg':
train_loader = DataLoader(
ScanObjectNN(root='data/ScanObjectNN/main_split', partition='train', num_points=args.num_points), num_workers=8,
batch_size=args.batch_size, shuffle=True, drop_last=True)
test_loader = DataLoader(ScanObjectNN(root='data/ScanObjectNN/main_split',partition='test', num_points=args.num_points), num_workers=8,
batch_size=args.test_batch_size, shuffle=False, drop_last=False)
elif args.dataset == 'ScanObjectNN_hardest':
train_loader = DataLoader(
ScanObjectNN_hardest(root='data/ScanObjectNN/main_split', partition='train', num_points=args.num_points), num_workers=8,
batch_size=args.batch_size, shuffle=True, drop_last=True)
test_loader = DataLoader(ScanObjectNN_hardest(root='data/ScanObjectNN/main_split', partition='test', num_points=args.num_points), num_workers=8,
batch_size=args.test_batch_size, shuffle=False, drop_last=False)
device = torch.device("cuda" if args.cuda else "cpu")
model_pretrain = MAE3D(args, encoder_dims=1024, decoder_dims=1024).to(device)
model_cls = MAE3D_cls(args, encoder_dims=1024).to(device)
print(str(model_pretrain))
model_pretrain = nn.DataParallel(model_pretrain)
print(str(model_cls))
model_cls = nn.DataParallel(model_cls)
if args.pretrained:
model_pretrain.load_state_dict(torch.load(pretrain_model_path))
print("Loaded model: %s" % (pretrain_model_path))
p_keys = []
f_keys = []
for p_key in model_pretrain.state_dict():
if len(p_key.split('.')) >= 3 and p_key.split('.')[2] == 'patch_embed':
p_keys.append(p_key)
for f_key in model_cls.state_dict():
if len(f_key.split('.')) >= 2 and f_key.split('.')[1] == 'patch_embed':
f_keys.append(f_key)
for (p_key, f_key) in zip(p_keys, f_keys):
model_cls.state_dict()[f_key].copy_(model_pretrain.state_dict()[p_key])
if args.linear_classifier:
for n, p in model_cls.named_parameters():
if n.split('.')[1] == 'patch_embed':
p.requires_grad = False
if args.use_sgd:
print("Use SGD")
opt = optim.SGD([p for p in model_cls.parameters() if p.requires_grad], lr=args.lr * 100,
momentum=args.momentum, weight_decay=5e-4)
else:
print("Use Adam")
opt = optim.Adam([p for p in model_cls.parameters() if p.requires_grad], lr=args.lr, weight_decay=1e-4)
scheduler = CosineAnnealingLR(opt, args.epochs, eta_min=args.lr)
best_test_acc = 0
for epoch in range(args.epochs):
# scheduler.step()
train_loss = 0.0
count = 0.0
model_cls.train()
train_pred = []
train_true = []
total_time = 0.0
for batch, (data, label) in enumerate(tqdm(train_loader)):
data, label = data.to(device), label.to(device).squeeze()
data = data.permute(0, 2, 1)
batch_size = data.size()[0]
opt.zero_grad()
start_time = time.time()
logits = model_cls(data)
loss = cal_loss(logits, label)
loss.backward()
opt.step()
end_time = time.time()
total_time += (end_time - start_time)
preds = logits.max(dim=1)[1]
count += batch_size
train_loss += loss.item() * batch_size
train_true.append(label.cpu().numpy())
train_pred.append(preds.detach().cpu().numpy())
scheduler.step()
print('train total time is', total_time)
train_true = np.concatenate(train_true)
train_pred = np.concatenate(train_pred)
outstr = 'Train %d, loss: %.6f, train acc: %.6f, train avg acc: %.6f' % (
epoch,
train_loss * 1.0 / count,
metrics.accuracy_score(
train_true, train_pred),
metrics.balanced_accuracy_score(
train_true, train_pred))
io.cprint(outstr)
####################
# Test
####################
test_loss = 0.0
count = 0.0
model_cls.eval()
test_pred = []
test_true = []
total_time = 0.0
for data, label in tqdm(test_loader):
data, label = data.to(device), label.to(device).squeeze()
data = data.permute(0, 2, 1)
batch_size = data.size()[0]
start_time = time.time()
logits = model_cls(data)
loss = cal_loss(logits, label)
end_time = time.time()
total_time += (end_time - start_time)
preds = logits.max(dim=1)[1]
count += batch_size
test_loss += loss.item() * batch_size
test_true.append(label.cpu().numpy())
test_pred.append(preds.detach().cpu().numpy())
print('test total time is', total_time)
test_true = np.concatenate(test_true)
test_pred = np.concatenate(test_pred)
test_acc = metrics.accuracy_score(test_true, test_pred)
avg_per_class_acc = metrics.balanced_accuracy_score(test_true, test_pred)
outstr = 'Test %s, loss: %.6f, test acc: %.6f, test avg acc: %.6f' % (
epoch,
test_loss * 1.0 / count,
test_acc,
avg_per_class_acc)
io.cprint(outstr)
if test_acc >= best_test_acc:
best_test_acc = test_acc
torch.save(model_cls.state_dict(), 'checkpoints/%s/models/model_cls.t7' % args.exp_name)
outstr = 'Best test acc: %.6f' % best_test_acc
io.cprint(outstr)
def test(args, io):
test_loader = DataLoader(ModelNet40(partition='test', num_points=args.num_points),
batch_size=args.test_batch_size, shuffle=True, drop_last=False)
device = torch.device("cuda" if args.cuda else "cpu")
model_cls = MAE3D_cls(args, encoder_dims=1024).to(device)
model_cls = nn.DataParallel(model_cls)
model_cls.load_state_dict(torch.load(model_path))
model_cls = model_cls.eval()
test_loss = 0.0
count = 0.0
model_cls.eval()
test_pred = []
test_true = []
total_time = 0.0
for data, label in tqdm(test_loader):
data, label = data.to(device), label.to(device).squeeze()
data = data.permute(0, 2, 1)
batch_size = data.size()[0]
start_time = time.time()
logits = model_cls(data)
loss = cal_loss(logits, label)
end_time = time.time()
total_time += (end_time - start_time)
preds = logits.max(dim=1)[1]
count += batch_size
test_loss += loss.item() * batch_size
test_true.append(label.cpu().numpy())
test_pred.append(preds.detach().cpu().numpy())
print('test total time is', total_time)
test_true = np.concatenate(test_true)
test_pred = np.concatenate(test_pred)
test_acc = metrics.accuracy_score(test_true, test_pred)
avg_per_class_acc = metrics.balanced_accuracy_score(test_true, test_pred)
outstr = 'Test :: loss: %.6f, test acc: %.6f, test avg acc: %.6f' % (
test_loss * 1.0 / count,
test_acc,
avg_per_class_acc)
io.cprint(outstr)
if __name__ == "__main__":
# Training settings
parser = argparse.ArgumentParser(description='Point Cloud Completion Pre-training')
parser.add_argument('--exp_name', type=str, default='exp_shapenet55_block', metavar='N',
help='Name of the experiment')
parser.add_argument('--mask_ratio', type=float, default=0.7, help='masking ratio')
parser.add_argument('--random', type=bool, default=False, metavar='N', help='random masking')
parser.add_argument('--patch_size', type=int, default=64, help='patch size')
parser.add_argument('--dataset', type=str, default='modelnet40', metavar='N',
choices=['modelnet40', 'ScanObjectNN_objectonly', 'ScanObjectNN_objectbg', 'ScanObjectNN_hardest'])
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=8, metavar='batch_size',
help='Size of batch)')
parser.add_argument('--epochs', type=int, default=251, metavar='N',
help='number of episode to train ')
parser.add_argument('--use_sgd', type=bool, default=False,
help='Use SGD')
parser.add_argument('--lr', type=float, default=0.0001, metavar='LR',
help='learning rate (default: 0.0001, 0.1 if using sgd)') # 0.00000001
parser.add_argument('--momentum', type=float, default=0.7, metavar='M',
help='SGD momentum (default: 0.9)')
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('--num_points', type=int, default=1024,
help='num of points to use')
parser.add_argument('--dropout', type=float, default=0.5,
help='dropout rate')
parser.add_argument('--limited_ratio', type=float, default=1.0,
help='dropout rate')
parser.add_argument('--eval', type=bool, default=False,
help='evaluate the model')
parser.add_argument('--pretrained', type=bool, default=False, metavar='N',
help='Restore model from path')
parser.add_argument('--finetune', type=bool, default=False, metavar='N',
help='Restore model from path')
parser.add_argument('--linear_classifier', type=bool, default=False, metavar='N',
help='random mask')
args = parser.parse_args()
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
file_name = 'mask_ratio_' + str(args.mask_ratio) + '/' + args.exp_name
pretrain_model_path = './checkpoints/mask_ratio_' + str(args.mask_ratio) + '/' + args.exp_name + '/models/model_pretrain.t7'
model_path = './checkpoints/mask_ratio_' + str(args.mask_ratio) + '/' + args.exp_name + '/models/model_cls.t7'
_init_()
io = IOStream('checkpoints/' + file_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)