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train.py
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train.py
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import os
import time
import shutil
import numpy as np
np.random.seed(0)
import torch
torch.manual_seed(0)
import torch.nn as nn
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter
from tqdm import tqdm
from dataset import ShapeNet
from model import CSGStumpNet
from loss import Loss
from config import Config
from utils import init
import argparse
device = torch.device("cuda")
def train(config):
init(config)
train_dataset = ShapeNet(partition='train', category=config.category, shapenet_root=config.dataset_root, balance=config.balance,num_surface_points=config.num_surface_points, num_sample_points=config.num_sample_points)
train_loader = DataLoader(train_dataset, pin_memory=True, num_workers=20, batch_size=config.train_batch_size_per_gpu*config.num_gpu, shuffle=True, drop_last=True)
test_dataset = ShapeNet(partition='val', category=config.category, shapenet_root=config.dataset_root, balance=config.balance,num_surface_points=config.num_surface_points, num_sample_points=config.num_sample_points)
test_loader = DataLoader(test_dataset, pin_memory=True, num_workers=20, batch_size=config.test_batch_size_per_gpu*config.num_gpu, shuffle=True, drop_last=True)
# clear pervious tensorboard entries
if os.path.exists("./runs/%s" % config.experiment_name):
shutil.rmtree("./runs/%s" % config.experiment_name)
writer = SummaryWriter("./runs/%s" % config.experiment_name)
# loading model
model = CSGStumpNet(config).to(device)
pre_train_model_path = './checkpoints/%s/models/model.th' % config.experiment_name
if not os.path.exists(pre_train_model_path):
print("Cannot find pre-train model for experiment: {}\n Training from scratch".format(config.experiment_name))
else:
print("Loading pre-train weights from {}".format(pre_train_model_path))
model.load_state_dict(torch.load('./checkpoints/%s/models/model.th' % config.experiment_name))
print("Let's use", torch.cuda.device_count(), "GPUs!")
model = nn.DataParallel(model)
# training settings
opt=torch.optim.Adam(model.parameters(), lr=config.learning_rate, betas=(config.beta1, 0.999))
criterion = Loss(config)
start_time = time.time()
eval_interval = config.eval_interval
train_counter = 0
test_counter = 0
current_loss_recon = np.inf
for epoch in range(config.epoch):
model.train()
avg_loss = avg_loss_recon = avg_loss_primitive = iter_counter = avg_accuracy = avg_recall = avg_fscore = avg_fscore = 0
print("Training: Epoch %d" % epoch)
train_loader_t = tqdm(train_loader)
for surface_pointcloud, testing_points in train_loader_t:
surface_pointcloud = surface_pointcloud.to(device)
testing_points = testing_points.to(device)
model.zero_grad()
occupancies, primitive_sdfs = model(surface_pointcloud.transpose(2,1), testing_points[:,:,:3], is_training=True)
predict_occupancies = (occupancies >=0.5).float()
target_occupancies = (testing_points[:,:,-1] >=0.5).float()
accuracy = torch.sum(predict_occupancies*target_occupancies)/torch.sum(target_occupancies)
recall = torch.sum(predict_occupancies*target_occupancies)/(torch.sum(predict_occupancies)+1e-9)
loss_dict = criterion(occupancies, testing_points[:,:,-1], primitive_sdfs)
loss_dict["loss_total"].backward()
train_loader_t.set_description("Loss Total: %f" % loss_dict["loss_total"].item())
opt.step()
# accumulate loss values
avg_loss += loss_dict["loss_total"].item()
avg_loss_recon += loss_dict["loss_recon"].item()
avg_loss_primitive += loss_dict["loss_primitive"].item()
avg_accuracy += accuracy.item()
avg_recall += recall.item()
fscore = 2*accuracy.item()*recall.item()/(accuracy.item() + recall.item() + 1e-6)
fscore = 0 if np.isnan(fscore) else fscore
avg_fscore += fscore
# update tensorboard
writer.add_scalar('Loss/train',loss_dict["loss_total"].item(), global_step=train_counter)
writer.add_scalar("Loss_Recon/train", loss_dict["loss_recon"].item(),global_step=train_counter)
writer.add_scalar("Loss_Primitive/train", loss_dict["loss_primitive"].item(),global_step=train_counter)
writer.add_scalar("accuracy/train", accuracy.item(), global_step=train_counter)
writer.add_scalar("recall/train", recall.item(), global_step=train_counter)
writer.add_scalar("fscore/train", fscore, global_step=train_counter)
iter_counter += 1
train_counter += 1
acc = avg_accuracy / iter_counter
recall = avg_recall / iter_counter
fscore = avg_fscore / iter_counter
print("Experiment: %s" % config.experiment_name)
print("Training: [%2d/%2d] time: %4.4f, loss_sp: %.6f, loss_total: %.6f, loss_primitive: %.6f, acc: %.6f, recall: %.6f, fscore: %.6f" % (epoch,
config.epoch,
time.time() - start_time,
avg_loss_recon / iter_counter,
avg_loss/iter_counter,
avg_loss_primitive/iter_counter,
acc,
recall,
fscore))
# Eval models
if (epoch+1) % eval_interval == 0:
model.eval()
with torch.no_grad():
testloader_t = tqdm(test_loader)
avg_test_loss_recon = avg_test_loss_primitive = avg_test_loss = test_iter_counter = avg_test_accuracy = avg_test_recall = 0
for surface_pointcloud, testing_points in testloader_t:
surface_pointcloud = surface_pointcloud.to(device)
testing_points = testing_points.to(device)
occupancies, primitive_sdfs = model(surface_pointcloud.transpose(2,1), testing_points[:,:,:3], is_training=False)
loss_dict = criterion(occupancies, testing_points[:,:,-1], primitive_sdfs)
predict_occupancies = (occupancies >=0.5).float()
target_occupancies = (testing_points[:,:,-1] >=0.5).float()
accuracy = torch.sum(predict_occupancies*target_occupancies)/torch.sum(target_occupancies)
recall = torch.sum(predict_occupancies*target_occupancies)/(torch.sum(predict_occupancies)+1e-9)
avg_test_loss_recon += loss_dict["loss_recon"].item()
avg_test_loss_primitive += loss_dict["loss_primitive"].item()
avg_test_loss += loss_dict["loss_total"].item()
avg_test_accuracy += accuracy.item()
avg_test_recall += recall.item()
# update tensorboard
writer.add_scalar('Loss/test', loss_dict["loss_total"].item(), global_step=test_counter)
writer.add_scalar("Loss_Primitive/test", loss_dict["loss_primitive"].item(),global_step=test_counter)
writer.add_scalar("Loss_Recon/test", loss_dict["loss_recon"].item(),global_step=test_counter)
writer.add_scalar("accuracy/test", accuracy.cpu().detach().numpy(),global_step=test_counter)
writer.add_scalar("recall/test", recall.cpu().detach().numpy(),global_step=test_counter)
writer.add_scalar("fscore/test", 2*accuracy.item()*recall.item()/(accuracy.item() + recall.item() + 1e-6), global_step=test_counter)
test_counter += 1
test_iter_counter += 1
avg_test_loss_recon = avg_test_loss_recon / test_iter_counter
test_accuracy = avg_test_accuracy / test_iter_counter
test_recall = avg_test_recall / test_iter_counter
test_fscore = 2*test_accuracy*test_recall/(test_accuracy + test_recall + 1e-6)
print("Testing: [%2d/%2d] time: %4.4f, loss_total: %.6f, loss_recon: %.6f, loss_primitive: %.6f, acc: %.6f, recall: %.6f, fscore: %.6f" % (epoch,
config.epoch,
time.time() - start_time,
avg_test_loss/test_iter_counter,
avg_test_loss_recon / test_iter_counter,
avg_test_loss_primitive/test_iter_counter,
test_accuracy,
test_recall,
test_fscore))
if avg_test_loss_recon < current_loss_recon:
current_loss_recon = avg_test_loss_recon
print("Updating model weights... Current best epoch: %d" % (epoch+1))
torch.save(model.module.state_dict(), './checkpoints/%s/models/model.th' % config.experiment_name)
if __name__ == "__main__":
parser = argparse.ArgumentParser(description='CSGStumpNet')
parser.add_argument('--config_path', type=str, default='./configs/config_default.json', metavar='N',
help='config_path')
args = parser.parse_args()
config = Config((args.config_path))
train(config)