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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 util.data_util import ModelNet40
from model.GDANet_cls import GDANET
import numpy as np
from torch.utils.data import DataLoader
from util.util import cal_loss, IOStream
import sklearn.metrics as metrics
# weight initialization:
def weight_init(m):
if isinstance(m, torch.nn.Linear):
torch.nn.init.xavier_normal_(m.weight)
torch.nn.init.constant_(m.bias, 0)
elif isinstance(m, torch.nn.Conv2d):
torch.nn.init.xavier_normal_(m.weight)
torch.nn.init.constant_(m.bias, 0)
elif isinstance(m, torch.nn.Conv1d):
torch.nn.init.xavier_normal_(m.weight)
torch.nn.init.constant_(m.bias, 0)
elif isinstance(m, torch.nn.BatchNorm2d):
torch.nn.init.constant_(m.weight, 1)
torch.nn.init.constant_(m.bias, 0)
elif isinstance(m, torch.nn.BatchNorm1d):
torch.nn.init.constant_(m.weight, 1)
torch.nn.init.constant_(m.bias, 0)
def _init_():
if not os.path.exists('checkpoints'):
os.makedirs('checkpoints')
if not os.path.exists('checkpoints/'+args.exp_name):
os.makedirs('checkpoints/'+args.exp_name)
# backup the running files:
if not args.eval:
os.system('cp main_cls.py checkpoints' + '/' + args.exp_name + '/' + 'main.py.backup')
os.system('cp model/GDANet_cls.py checkpoints' + '/' + args.exp_name + '/' + 'GDANet_cls.py.backup')
os.system('cp util.GDANet_util.py checkpoints' + '/' + args.exp_name + '/' + 'GDANet_util.py.backup')
os.system('cp util.data_util.py checkpoints' + '/' + args.exp_name + '/' + 'data_util.py.backup')
def train(args, io):
train_loader = DataLoader(ModelNet40(partition='train', num_points=args.num_points), num_workers=8,
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)
device = torch.device("cuda" if args.cuda else "cpu")
model = GDANET().to(device)
print(str(model))
model.apply(weight_init)
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)
scheduler = CosineAnnealingLR(opt, args.epochs, eta_min=args.lr)
else:
print("Use Adam")
opt = optim.Adam(model.parameters(), lr=args.lr, weight_decay=1e-4)
scheduler = CosineAnnealingLR(opt, args.epochs, eta_min=args.lr/100)
criterion = cal_loss
best_test_acc = 0
for epoch in range(args.epochs):
scheduler.step()
####################
# Train
####################
train_loss = 0.0
count = 0.0
model.train()
train_pred = []
train_true = []
for data, label in 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()
logits = model(data)
loss = criterion(logits, label)
loss.backward()
opt.step()
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())
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.eval()
test_pred = []
test_true = []
for data, label in test_loader:
data, label = data.to(device), label.to(device).squeeze()
data = data.permute(0, 2, 1)
batch_size = data.size()[0]
logits = model(data)
loss = criterion(logits, label)
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())
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 %d, 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
io.cprint('Max Acc:%.6f' % best_test_acc)
torch.save(model.state_dict(), 'checkpoints/%s/best_model.t7' % args.exp_name)
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 = GDANET().to(device)
model = nn.DataParallel(model)
model.load_state_dict(torch.load(args.model_path))
model = model.eval()
test_acc = 0.0
count = 0.0
test_true = []
test_pred = []
for data, label in test_loader:
data, label = data.to(device), label.to(device).squeeze()
data = data.permute(0, 2, 1)
batch_size = data.size()[0]
logits = model(data)
preds = logits.max(dim=1)[1]
test_true.append(label.cpu().numpy())
test_pred.append(preds.detach().cpu().numpy())
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 :: test acc: %.6f, test avg acc: %.6f'%(test_acc, avg_per_class_acc)
io.cprint(outstr)
if __name__ == "__main__":
# Training settings
parser = argparse.ArgumentParser(description='3D Object Classification')
parser.add_argument('--exp_name', type=str, default='GDANet', metavar='N',
help='Name of the experiment')
parser.add_argument('--batch_size', type=int, default=64, metavar='batch_size',
help='Size of batch)')
parser.add_argument('--test_batch_size', type=int, default=32, metavar='batch_size',
help='Size of batch)')
parser.add_argument('--epochs', type=int, default=350, 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('--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=1024,
help='num of points to use')
parser.add_argument('--model_path', type=str, default='', metavar='N',
help='Pretrained model path')
args = parser.parse_args()
_init_()
if not args.eval:
io = IOStream('checkpoints/' + args.exp_name + '/%s_train.log' % (args.exp_name))
else:
io = IOStream('checkpoints/' + args.exp_name + '/%s_test.log' % (args.exp_name))
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)