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main_cls.py
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main_cls.py
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"""
@Author: Yue Wang
@Contact: [email protected]
@File: main_cls.py
@Time: 2018/10/13 10:39 PM
Modified by
@Author: Manxi Lin
@Contact: [email protected]
@Time: 2022/07/07 17:10 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
from data_utils import ModelNet40, ModelNet40C, ModelNet40Noise, ModelNet40Resplit, ScanObjectNN
from models.diffConv_cls import Model
import numpy as np
from torch.utils.data import DataLoader
from misc import cal_loss, IOStream
import sklearn.metrics as metrics
torch.cuda.synchronize()
def train(args, io):
if args.dataset == 'modelnet40':
train_loader = DataLoader(ModelNet40(partition='train', num_points=args.num_points), num_workers=32,
batch_size=args.batch_size, shuffle=True, drop_last=True)
test_loader = DataLoader(ModelNet40(partition='test', num_points=args.num_points), num_workers=32,
batch_size=args.test_batch_size, shuffle=True, drop_last=False)
output_channels = 40
elif args.dataset == 'modelnet40resplit':
train_loader = DataLoader(ModelNet40Resplit(partition='train', num_points=args.num_points), num_workers=32,
batch_size=args.batch_size, shuffle=True, drop_last=True)
test_loader = DataLoader(ModelNet40Resplit(partition='vali', num_points=args.num_points), num_workers=32,
batch_size=args.test_batch_size, shuffle=True, drop_last=False)
output_channels = 40
elif args.dataset == 'scanobjectnn':
train_loader = DataLoader(ScanObjectNN(partition='train', num_points=args.num_points, bg=args.bg), num_workers=32,
batch_size=args.batch_size, shuffle=True, drop_last=True)
test_loader = DataLoader(ScanObjectNN(partition='test', num_points=args.num_points, bg=args.bg), num_workers=32,
batch_size=args.test_batch_size, shuffle=True, drop_last=False)
output_channels = 15
device = "cuda" if torch.cuda.is_available() else "cpu"
model = Model(args, output_channels)
print('begin experiment: %s'%args.exp_name)
model = model.to(device)
print(str(model))
model = nn.DataParallel(model)
print("Let's use", torch.cuda.device_count(), "GPUs!")
opt = optim.SGD(model.parameters(), lr=args.lr*100, momentum=args.momentum, weight_decay=1e-4)
scheduler = CosineAnnealingLR(opt, args.epochs, eta_min=1e-3)
criterion = cal_loss
best_test_acc = 0
for epoch in range(args.epochs):
####################
# 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()
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())
scheduler.step()
train_true = np.concatenate(train_true)
train_pred = np.concatenate(train_pred)
outstr = 'Train %d, loss: %.4f, train acc: %.4f, train avg acc: %.4f' % (epoch,
train_loss*1.0/count,
metrics.accuracy_score(
train_true, train_pred),
metrics.balanced_accuracy_score(
train_true, train_pred))
io.cprint(outstr)
####################
# Validation
####################
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()
batch_size = data.size()[0]
with torch.no_grad():
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: %.4f, test acc: %.4f, test avg acc: %.4f' % (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.state_dict(), './checkpoints/%s.pth' % args.exp_name)
def test(args, io):
if args.dataset == 'modelnet40':
test_loader = DataLoader(ModelNet40(partition='test', num_points=args.num_points), num_workers=32,
batch_size=args.test_batch_size, shuffle=False, drop_last=False)
output_channels = 40
elif args.dataset == 'modelnet40C':
test_loader = DataLoader(ModelNet40C(args.corruption, args.severity), num_workers=32,
batch_size=args.test_batch_size, shuffle=False, drop_last=False)
output_channels = 40
elif args.dataset == 'modelnet40noise':
test_loader = DataLoader(ModelNet40Noise(args.num_points, args.num_noise), num_workers=32,
batch_size=args.test_batch_size, shuffle=False, drop_last=False)
output_channels = 40
elif args.dataset == 'modelnet40resplit':
test_loader = DataLoader(ModelNet40Resplit(partition='test', num_points=args.num_points), num_workers=32,
batch_size=args.test_batch_size, shuffle=False, drop_last=False)
output_channels = 40
elif args.dataset == 'scanobjectnn':
test_loader = DataLoader(ScanObjectNN(partition='test', num_points=args.num_points, bg=args.bg), num_workers=32,
batch_size=args.test_batch_size, shuffle=False, drop_last=False)
output_channels = 15
device = "cuda" if torch.cuda.is_available() else "cpu"
#Try to load models
model = Model(args, output_channels)
model = model.to(device)
model = nn.DataParallel(model)
model.load_state_dict(torch.load(args.model_path))
model = model.eval()
test_acc = 0.0
test_true = []
test_pred = []
for data, label in test_loader:
data, label = data.to(device), label.to(device).squeeze()
with torch.no_grad():
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: %.3f, test avg acc: %.3f'%(test_acc, avg_per_class_acc)
io.cprint(outstr)
if __name__ == "__main__":
# Training settings
parser = argparse.ArgumentParser(description='Point Cloud Recognition')
parser.add_argument('--exp_name', type=str, default='exp', metavar='N',
help='Name of the experiment')
parser.add_argument('--dataset', type=str, default='modelnet40', metavar='N',
choices=['modelnet40', 'modelnet40C', 'modelnet40noise', 'modelnet40resplit', 'scanobjectnn'])
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=2000, metavar='N',
help='number of episode to train')
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('--seed', type=int, default=42, metavar='S',
help='random seed (default: 42)')
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('--dropout', type=float, default=0.5,
help='initial dropout rate')
parser.add_argument('--emb_dims', type=int, default=512, metavar='N',
help='Dimension of embeddings')
parser.add_argument('--model_path', type=str, default='', metavar='N',
help='Pretrained model path')
parser.add_argument('--radius', type=float, default=0.005,
help='search radius')
parser.add_argument('--corruption', type=str, default='uniform', metavar='N',
help='corruption of ModelNetC')
parser.add_argument('--severity', type=int, default=1, metavar='S',
help='severity of ModelNetC')
parser.add_argument('--num_noise', type=int, default=100,
help='number of noise points in noise study')
parser.add_argument('--bg', type=bool, default=False,
help='whether to add background in scanobjectnn')
args = parser.parse_args()
io = IOStream(os.path.join('./logs', args.exp_name))
io.cprint(str(args))
torch.manual_seed(args.seed)
if torch.cuda.is_available():
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)