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main_pointnet.py
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main_pointnet.py
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from model.pointnet_sem_seg import get_model
from dataset.tdmatch import DentalMeshDataset, DentalMeshSampledDataset
from torch.utils.data import DataLoader
import torch
from torch import optim
import torch.nn as nn
import os
import torch.optim.lr_scheduler as lr_scheduler
from scheduler import CosineLRScheduler
import numpy as np
import math
from time import time
import loss_segmentation
from torch.utils.tensorboard import SummaryWriter
import argparse
# import open3d as o3d
# palet = torch.tensor([
# [255,153,153],
# [153,76,0],
# [153,153,0],
# [76,153,0],
# [0,153,153],
# [0,0,153],
# [153,0,153],
# [153,0,76],
# [64,64,64],
# [60, 30, 0],
# [60, 60, 0],
# [30, 60, 0],
# [0, 60, 60],
# [0, 0, 60],
# [60, 0, 60],
# [60, 0, 30],
# [30, 30, 30],
# ]).cuda()/255
parser = argparse.ArgumentParser()
parser.add_argument('--resume', default=None, type=str)
parser.add_argument('--lr_head', action='store_true')
args = parser.parse_args()
dir_name = 'vertexnorm'
print("<", dir_name, ">")
if not os.path.exists(os.path.join('checkpoints_pointnet', dir_name)):
os.mkdir(os.path.join('checkpoints_pointnet', dir_name))
if __name__ == '__main__':
writer = SummaryWriter(os.path.join('runs_pointnet', dir_name))
os.environ["CUDA_DEVICE_ORDER"]="PCI_BUS_ID"
# os.environ["CUDA_VISIBLE_DEVICES"]= "0, 1"
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print('Device:', device) # 출력결과: cuda
print('Count of using GPUs:', torch.cuda.device_count()) #출력결과: 2 (0, 1 두개 사용하므로)
print('Current cuda device:', torch.cuda.current_device()) # 출력결과: 0 (0, 1 중 앞의 GPU #0 의미)
model = get_model(17)
model.cuda()
batch_size = 1
# training_set = DataLoader(DentalMeshDataset(split_with_txt_path='base_name_train_fold.txt', augmentation=True), batch_size=batch_size, shuffle=True, num_workers=0)
# validation_set = DataLoader(DentalMeshDataset(split_with_txt_path='base_name_test_fold.txt', augmentation=False), batch_size=batch_size, shuffle=False, num_workers=0)
training_set = DataLoader(DentalMeshSampledDataset(split_with_txt_path='base_name_train_fold.txt', augmentation=True), batch_size=batch_size, shuffle=True, num_workers=0)
validation_set = DataLoader(DentalMeshSampledDataset(split_with_txt_path='base_name_test_fold.txt', augmentation=False), batch_size=batch_size, shuffle=False, num_workers=0)
# training_set = DataLoader(DentalMeshSampledDataset(split_with_txt_path='base_name_train_fold_osstem.txt', augmentation=True), batch_size=batch_size, shuffle=True, num_workers=0)
# validation_set = DataLoader(DentalMeshSampledDataset(split_with_txt_path='base_name_test_fold_osstem.txt', augmentation=False), batch_size=batch_size, shuffle=False, num_workers=0)
start_epoch = 0
epochs = 100
optimizer = optim.SGD(
model.parameters(),
lr = 1e-2,
momentum=0.9,
weight_decay=0.0001
)
scheduler = lr_scheduler.MultiStepLR(optimizer, milestones=[int(epochs * 0.6), int(epochs * 0.8)], gamma=0.1)
if args.resume:
checkpoint = torch.load(os.path.join('checkpoints_pointnet', dir_name, args.resume))
model.load_state_dict(checkpoint['model_state_dict'])
optimizer.load_state_dict(checkpoint['optimizer_state_dict'])
scheduler.load_state_dict(checkpoint['scheduler_state_dict'])
start_epoch = checkpoint['epoch'] + 1
# shceduler = CosineLRScheduler(
# optimizer,
# t_initial=40,
# lr_min=1e-5,
# warmup_lr_init=1e-6,
# warmup_t=0,
# k_decay=1.0,
# cycle_mul=1,
# cycle_decay=0.1,
# cycle_limit=1,
# noise_range_t=None,
# noise_pct=0.67,
# noise_std=1.,
# noise_seed=42,
# )
# criterion = nn.CrossEntropyLoss().cuda()
min_val_loss = math.inf
max_val_cls_acc = 0.0
for epoch in range(start_epoch, epochs):
train_loss = 0.0
model.train()
lr = optimizer.param_groups[0]['lr']
print()
print(f"Start Training! lr : [{lr}]")
for i, data in enumerate(training_set):
start_time = time()
src_pcd, src_normals, src_feats, src_raw_pcd, labels = data
src_pcd, src_normals, src_feats, src_raw_pcd, labels = src_pcd[0], src_normals[0], src_feats[0], src_raw_pcd[0], labels[0]
src_o = torch.tensor([src_raw_pcd.shape[0]]).to(src_raw_pcd).int()
src_raw_pcd = src_raw_pcd.unsqueeze(0).permute(0, 2, 1)
cls_output = model(src_raw_pcd)[0]
train_cls_loss = loss_segmentation.tooth_class_loss(cls_output, labels, 17)
train_total_loss = train_cls_loss
optimizer.zero_grad()
train_total_loss.backward()
optimizer.step()
train_loss += train_total_loss
if (i+1) % 20 == 0:
print("Epoch: [{}/{}][{}/{}] Cls_loss: {train_cls_loss:.6f} Total_loss: {train_total_loss:.6f}"
.format(epoch+1, epochs, i+1, len(training_set),
train_cls_loss=train_cls_loss.item(),
train_total_loss=train_total_loss.item()))
end_time = time()
scheduler.step()
with torch.no_grad():
val_loss = 0.0
val_total_cls_acc = 0.0
val_total_mask_acc = 0.0
val_total_lr_acc = 0.0
model.eval()
print()
print("Start Validation!")
for i, data in enumerate(validation_set):
src_pcd, src_normals, src_feats, src_raw_pcd, labels = data
src_pcd, src_normals, src_feats, src_raw_pcd, labels = src_pcd[0], src_normals[0], src_feats[0], src_raw_pcd[0], labels[0]
src_o = torch.tensor([src_raw_pcd.shape[0]]).to(src_raw_pcd).int()
src_raw_pcd = src_raw_pcd.unsqueeze(0).permute(0, 2, 1)
cls_output = model(src_raw_pcd)[0]
val_cls_loss = loss_segmentation.tooth_class_loss(cls_output, labels, 17)
val_total_loss = val_cls_loss
val_loss += val_total_loss
val_cls_acc = (cls_output.argmax(-1) == (labels).reshape(-1)).sum() / len(labels)
val_total_cls_acc += val_cls_acc
if (i+1) % 20 == 0:
print("Epoch: [{}/{}][{}/{}] Cls_loss: {val_cls_loss:.6f} Total_loss: {val_total_loss:.6f} | Cls_acc: {val_cls_acc:.6f}"
.format(epoch+1, epochs, i+1, len(validation_set),
val_cls_loss=val_cls_loss.item(),
val_total_loss=val_total_loss.item(),
val_cls_acc=val_cls_acc.item()))
val_losses = val_loss / len(validation_set)
val_cls_accs = val_total_cls_acc / len(validation_set)
val_mask_accs = val_total_mask_acc / len(validation_set)
if args.lr_head:
val_lr_accs = val_total_lr_acc / len(validation_set)
writer.add_scalar("Loss/Train", train_loss / len(training_set), epoch+1)
writer.add_scalar("Loss/Validation", val_losses, epoch+1)
writer.add_scalar("Loss/Class Accuracy", val_cls_accs, epoch+1)
if args.lr_head:
writer.add_scalar("Loss/Left-Right Accuracy", val_lr_accs, epoch+1)
print("Epoch: [{}/{}] Training Loss: {train_loss:.6f}, Validation Loss : {val_loss:.6f}, Validation Cls Accuracy : {val_cls_accs:.6f}".format(epoch+1, epochs,
train_loss=train_loss / len(training_set),
val_loss=val_losses,
val_cls_accs=val_cls_accs))
if min_val_loss > val_losses or max_val_cls_acc < val_cls_accs:
print(f'Loss({min_val_loss:.6f}--->{val_losses:.6f}) Class Accuracy({max_val_cls_acc:.6f}--->{val_cls_accs:.6f})\t Saving The Model')
if min_val_loss > val_losses:
min_val_loss = val_losses
if max_val_cls_acc < val_cls_accs:
max_val_cls_acc = val_cls_accs
torch.save({
'epoch': epoch,
'model_state_dict': model.state_dict(),
'optimizer_state_dict': optimizer.state_dict(),
'loss': val_losses,
'cls_acc': val_cls_accs,
'mask_accs': val_mask_accs},
f'checkpoints_pointnet/{dir_name}/epoch{epoch+1}_val{val_losses:.4f}_cls_acc{val_cls_accs:.4f}_mask_acc{val_mask_accs:.4f}.pth')
writer.flush()
writer.close()