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main.py
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#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
RSMix:
@Author: Dogyoon Lee
@Contact: [email protected]
@File: main.py
@Time: 2020/11/23 13:46 PM
DGCNN:
@Author: Yue Wang
@Contact: [email protected]
@File: main.py
@Time: 2018/10/13 10:39 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 import ModelNet40
from model import PointNet, DGCNN
import numpy as np
from torch.utils.data import DataLoader
from util import cal_loss, IOStream
import sklearn.metrics as metrics
import time
from datetime import datetime
import provider
import rsmix_provider
from ModelNetDataLoader import ModelNetDataLoader
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)
if not os.path.exists('checkpoints/'+args.exp_name+'/'+'models'):
os.makedirs('checkpoints/'+args.exp_name+'/'+'models')
os.system('cp main.py checkpoints'+'/'+args.exp_name+'/'+'main.py.backup')
os.system('cp model.py checkpoints' + '/' + args.exp_name + '/' + 'model.py.backup')
os.system('cp util.py checkpoints' + '/' + args.exp_name + '/' + 'util.py.backup')
os.system('cp data.py checkpoints' + '/' + args.exp_name + '/' + 'data.py.backup')
def log_string(out_str):
LOG_FOUT.write(out_str+'\n')
LOG_FOUT.flush()
print(out_str)
def train(args, io):
if args.modelnet10:
TRAIN_DATASET = ModelNetDataLoader(root=args.data_path, npoint=args.num_points, split='train', normal_channel=args.normal, modelnet10=True)
TEST_DATASET = ModelNetDataLoader(root=args.data_path, npoint=args.num_points, split='test', normal_channel=args.normal, modelnet10=True)
train_loader = DataLoader(TRAIN_DATASET, batch_size=args.batch_size, shuffle=True, num_workers=8, drop_last=True)
test_loader = DataLoader(TEST_DATASET, batch_size=args.test_batch_size, shuffle=False, num_workers=8, drop_last=False)
num_class = 10
else:
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)
num_class = 40
# drop last : don't use the last batch if the size of it is different to the other ones(when its True)
device = torch.device("cuda" if args.cuda else "cpu")
#Try to load models
if args.model == 'pointnet':
model = PointNet(args).to(device)
elif args.model == 'dgcnn':
model = DGCNN(args, output_channels=num_class).to(device)
else:
raise Exception("Not implemented")
print(str(model))
# model = nn.DataParallel(model) # for multi-gpu
# 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)
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)
criterion = cal_loss
best_test_acc = 0
best_avg_class_acc = 0
conv_epoch = 0
for epoch in range(args.epochs):
log_string(str(datetime.now()))
log_string('**** EPOCH %03d ****' % (epoch))
scheduler.step()
####################
# Train
####################
train_loss = 0.0
count = 0.0
model.train()
train_pred = []
train_true = []
for data, label in train_loader:
'''
implement augmentation
'''
rsmix = False
# for new augmentation code, remove squeeze because it will be applied after augmentation.
# default from baseline model, scale, shift, shuffle was default augmentation
if args.rot or args.rdscale or args.shift or args.jitter or args.shuffle or args.rddrop or (args.beta is not 0.0):
data = data.cpu().numpy()
if args.rot:
data = provider.rotate_point_cloud(data)
data = provider.rotate_perturbation_point_cloud(data)
if args.rdscale:
tmp_data = provider.random_scale_point_cloud(data[:,:,0:3])
data[:,:,0:3] = tmp_data
if args.shift:
tmp_data = provider.shift_point_cloud(data[:,:,0:3])
data[:,:,0:3] = tmp_data
if args.jitter:
tmp_data = provider.jitter_point_cloud(data[:,:,0:3])
data[:,:,0:3] = tmp_data
if args.rddrop:
data = provider.random_point_dropout(data)
if args.shuffle:
data = provider.shuffle_points(data)
r = np.random.rand(1)
if args.beta > 0 and r < args.rsmix_prob:
rsmix = True
data, lam, label, label_b = rsmix_provider.rsmix(data, label, beta=args.beta, n_sample=args.nsample, KNN=args.knn)
if args.rot or args.rdscale or args.shift or args.jitter or args.shuffle or args.rddrop or (args.beta is not 0.0):
data = torch.FloatTensor(data)
if rsmix:
lam = torch.FloatTensor(lam)
lam, label_b = lam.to(device), label_b.to(device).squeeze()
data, label = data.to(device), label.to(device).squeeze()
if rsmix:
data = data.permute(0, 2, 1)
batch_size = data.size()[0]
opt.zero_grad()
logits = model(data)
loss = 0
for i in range(batch_size):
loss_tmp = criterion(logits[i].unsqueeze(0), label[i].unsqueeze(0).long())*(1-lam[i]) \
+ criterion(logits[i].unsqueeze(0), label_b[i].unsqueeze(0).long())*lam[i]
loss += loss_tmp
loss = loss/batch_size
else:
data = data.permute(0, 2, 1)
batch_size = data.size()[0]
opt.zero_grad()
logits = model(data)
loss = criterion(logits, label)
'''
from above to here
'''
# 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)
train_acc = metrics.accuracy_score(train_true, train_pred)
outstr = 'Train epoch %d, loss: %.6f, train acc: %.6f, train avg acc: %.6f' % (epoch,
train_loss*1.0/count,
train_acc,
metrics.balanced_accuracy_score(
train_true, train_pred))
io.cprint(outstr)
LOG_FOUT.write(outstr+'\n')
LOG_FOUT.flush()
####################
# Test
####################
log_string('---- EPOCH %03d EVALUATION ----'%(epoch))
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 epoch %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)
LOG_FOUT.write(outstr+'\n')
LOG_FOUT.flush()
if test_acc >= best_test_acc:
best_test_acc = test_acc
conv_epoch = epoch
torch.save(model.state_dict(), 'checkpoints/%s/models/model.t7' % args.exp_name)
log_string('Model saved in file : checkpoints/%s/models/model.t7' %(args.exp_name))
# if avg_per_class_acc >= best_avg_class_acc:
best_avg_class_acc = avg_per_class_acc
# torch.save(model.state_dict(), 'checkpoints/%s/models/model.t7' % args.exp_name)
# log_string('Model class_acc saved in file : checkpoints/%s/models/model_class_acc.t7' %(args.exp_name))
log_string('*** best accuracy *** - %f' %(best_test_acc))
log_string('*** at then, best class accuracy *** - %f' %(best_avg_class_acc))
execution_time = time.time()-start_time
hour = execution_time//3600
minute = (execution_time-hour*3600)//60
second = execution_time-hour*3600-minute*60
log_string('... End of the Training ...')
log_string("trainig time : %.2f sec, %d min, %d hour" %(float(second), int(minute), int(hour)))
log_string('*** training accuracy when best accuracy *** - %f' %(train_acc))
log_string('*** best accuracy *** - %f' %(best_test_acc))
log_string('*** at then, best class accuracy *** - %f' %(best_avg_class_acc))
log_string('*** conv epoch *** - %d' %(conv_epoch))
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")
#Try to load models
model = DGCNN(args).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='Point Cloud Recognition')
parser.add_argument('--exp_name', type=str, default='exp', metavar='N',
help='Name of the experiment')
parser.add_argument('--model', type=str, default='dgcnn', metavar='N',
choices=['pointnet', 'dgcnn'],
help='Model to use, [pointnet, dgcnn]')
parser.add_argument('--dataset', type=str, default='modelnet40', metavar='N',
choices=['modelnet40'])
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=250, 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('--dropout', type=float, default=0.5,
help='dropout rate')
parser.add_argument('--emb_dims', type=int, default=1024, metavar='N',
help='Dimension of embeddings')
parser.add_argument('--k', type=int, default=20, metavar='N',
help='Num of nearest neighbors to use')
parser.add_argument('--model_path', type=str, default='', metavar='N',
help='Pretrained model path')
# added arguments
parser.add_argument('--rdscale', action='store_true', help='random scaling data augmentation')
parser.add_argument('--shift', action='store_true', help='random shift data augmentation')
parser.add_argument('--shuffle', action='store_true', help='random shuffle data augmentation')
parser.add_argument('--rot', action='store_true', help='random rotation augmentation')
parser.add_argument('--jitter', action='store_true', help='jitter augmentation')
parser.add_argument('--rddrop', action='store_true', help='random point drop data augmentation')
parser.add_argument('--rsmix_prob', type=float, default=0.5, help='rsmix probability')
parser.add_argument('--beta', type=float, default=0.0, help='scalar value for beta function')
parser.add_argument('--nsample', type=float, default=512, help='default max sample number of the erased or added points in rsmix')
parser.add_argument('--modelnet10', action='store_true', help='use modelnet10')
parser.add_argument('--normal', action='store_true', help='use normal')
parser.add_argument('--knn', action='store_true', help='use knn instead ball-query function')
parser.add_argument('--data_path', type=str, default='./data/modelnet40_normal_resampled', help='dataset path')
args = parser.parse_args()
_init_()
io = IOStream('checkpoints/' + args.exp_name + '/run.log')
io.cprint(str(args))
args.cuda = not args.no_cuda and torch.cuda.is_available()
torch.manual_seed(args.seed)
if not os.path.exists('./log'): os.mkdir('./log')
LOG_DIR = os.path.join('./log',args.exp_name)
if not os.path.exists(LOG_DIR): os.mkdir(LOG_DIR)
LOG_FOUT = open(os.path.join(LOG_DIR, 'log_train.txt'), 'w')
LOG_FOUT.write(str(args)+'\n')
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')
start_time = time.time()
if not args.eval:
train(args, io)
else:
test(args, io)
LOG_FOUT.close()