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pc2cad.py
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pc2cad.py
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import torch.nn as nn
import torch
import numpy as np
import os
from torch.utils.data import Dataset, DataLoader
from tqdm import tqdm
import argparse
import h5py
import shutil
import json
import random
import sys
sys.path.append("..")
from trainer.base import BaseTrainer
from utils import cycle, ensure_dirs, ensure_dir, read_ply, write_ply
try:
from pointnet2_ops.pointnet2_modules import PointnetFPModule, PointnetSAModule
except Exception as e:
print("need to install https://github.com/erikwijmans/Pointnet2_PyTorch")
exit()
class Config(object):
n_points = 2048
batch_size = 128
num_workers = 4
nr_epochs = 200
lr = 1e-4
lr_step_size = 50
# beta1 = 0.5
grad_clip = None
save_frequency = 100
val_frequency = 10
def __init__(self, args):
self.data_root = os.path.join(args.proj_dir, args.exp_name, "results/all_zs_ckpt{}.h5".format(args.ae_ckpt))
self.pc_root = args.pc_root
self.split_path = args.split_path
self.exp_dir = os.path.join(args.proj_dir, args.exp_name, "pc2cad")
self.log_dir = os.path.join(self.exp_dir, 'log')
self.model_dir = os.path.join(self.exp_dir, 'model')
self.gpu_ids = args.gpu_ids
if (not args.test) and args.cont is not True and os.path.exists(self.exp_dir):
response = input('Experiment log/model already exists, overwrite? (y/n) ')
if response != 'y':
exit()
shutil.rmtree(self.exp_dir)
ensure_dirs([self.log_dir, self.model_dir])
if not args.test:
os.system("cp pc2cad.py {}".format(self.exp_dir))
with open('{}/config.txt'.format(self.exp_dir), 'w') as f:
json.dump(self.__dict__, f, indent=2)
class PointNet2(nn.Module):
def __init__(self):
super(PointNet2, self).__init__()
self.use_xyz = True
self._build_model()
def _build_model(self):
self.SA_modules = nn.ModuleList()
self.SA_modules.append(
PointnetSAModule(
npoint=512,
radius=0.1,
nsample=64,
mlp=[0, 32, 32, 64],
# bn=False,
use_xyz=self.use_xyz,
)
)
self.SA_modules.append(
PointnetSAModule(
npoint=256,
radius=0.2,
nsample=64,
mlp=[64, 64, 64, 128],
# bn=False,
use_xyz=self.use_xyz,
)
)
self.SA_modules.append(
PointnetSAModule(
npoint=128,
radius=0.4,
nsample=64,
mlp=[128, 128, 128, 256],
# bn=False,
use_xyz=self.use_xyz,
)
)
self.SA_modules.append(
PointnetSAModule(
mlp=[256, 256, 512, 1024],
# bn=False,
use_xyz=self.use_xyz
)
)
self.fc_layer = nn.Sequential(
nn.Linear(1024, 512),
nn.LeakyReLU(True),
nn.Linear(512, 256),
nn.LeakyReLU(True),
nn.Linear(256, 256),
nn.Tanh()
)
def _break_up_pc(self, pc):
xyz = pc[..., 0:3].contiguous()
features = pc[..., 3:].transpose(1, 2).contiguous() if pc.size(-1) > 3 else None
return xyz, features
def forward(self, pointcloud):
r"""
Forward pass of the network
Parameters
----------
pointcloud: Variable(torch.cuda.FloatTensor)
(B, N, 3 + input_channels) tensor
Point cloud to run predicts on
Each point in the point-cloud MUST
be formated as (x, y, z, features...)
"""
xyz, features = self._break_up_pc(pointcloud)
for module in self.SA_modules:
xyz, features = module(xyz, features)
return self.fc_layer(features.squeeze(-1))
class TrainAgent(BaseTrainer):
def build_net(self, config):
self.net = PointNet2().cuda()
def set_loss_function(self):
self.criterion = nn.MSELoss().cuda()
def set_optimizer(self, config):
"""set optimizer and lr scheduler used in training"""
self.optimizer = torch.optim.Adam(self.net.parameters(), config.lr) # , betas=(config.beta1, 0.9))
self.scheduler = torch.optim.lr_scheduler.StepLR(self.optimizer, config.lr_step_size)
def forward(self, data):
points = data["points"].cuda()
code = data["code"].cuda()
pred_code = self.net(points)
loss = self.criterion(pred_code, code)
return pred_code, {"mse": loss}
class ShapeCodesDataset(Dataset):
def __init__(self, phase, config):
super(ShapeCodesDataset, self).__init__()
self.n_points = config.n_points
self.data_root = config.data_root
self.pc_root = config.pc_root
self.path = config.split_path
with open(self.path, "r") as fp:
self.all_data = json.load(fp)[phase]
with h5py.File(self.data_root, 'r') as fp:
self.zs = fp["{}_zs".format(phase)][:]
def __getitem__(self, index):
data_id = self.all_data[index]
pc_path = os.path.join(self.pc_root, data_id + '.ply')
if not os.path.exists(pc_path):
return self.__getitem__(index + 1)
pc = read_ply(pc_path)
sample_idx = random.sample(list(range(pc.shape[0])), self.n_points)
pc = pc[sample_idx]
pc = torch.tensor(pc, dtype=torch.float32)
shape_code = torch.tensor(self.zs[index], dtype=torch.float32)
return {"points": pc, "code": shape_code, "id": data_id}
def __len__(self):
return len(self.zs)
def get_dataloader(phase, config, shuffle=None):
is_shuffle = phase == 'train' if shuffle is None else shuffle
dataset = ShapeCodesDataset(phase, config)
dataloader = DataLoader(dataset, batch_size=config.batch_size, shuffle=is_shuffle, num_workers=config.num_workers)
return dataloader
parser = argparse.ArgumentParser()
parser.add_argument('--proj_dir', type=str, default="proj_log",
help="path to project folder where models and logs will be saved")
parser.add_argument('--pc_root', type=str, default="path_to_pc_data", help="path to point clouds data folder")
parser.add_argument('--split_path', type=str, default="data/train_val_test_split.json", help="path to train-val-test split")
parser.add_argument('--exp_name', type=str, required=True, help="name of this experiment")
parser.add_argument('--ae_ckpt', type=str, required=True, help="desired checkpoint to restore")
parser.add_argument('--continue', dest='cont', action='store_true', help="continue training from checkpoint")
parser.add_argument('--ckpt', type=str, default='latest', required=False, help="desired checkpoint to restore")
parser.add_argument('--test',action='store_true', help="test mode")
parser.add_argument('--n_samples', type=int, default=100, help="number of samples to generate when testing")
parser.add_argument('-g', '--gpu_ids', type=str, default="0",
help="gpu to use, e.g. 0 0,1,2. CPU not supported.")
args = parser.parse_args()
if args.gpu_ids is not None:
os.environ["CUDA_VISIBLE_DEVICES"] = str(args.gpu_ids)
cfg = Config(args)
print("data path:", cfg.data_root)
agent = TrainAgent(cfg)
if not args.test:
# load from checkpoint if provided
if args.cont:
agent.load_ckpt(args.ckpt)
# create dataloader
train_loader = get_dataloader('train', cfg)
val_loader = get_dataloader('validation', cfg)
val_loader = cycle(val_loader)
# start training
clock = agent.clock
for e in range(clock.epoch, cfg.nr_epochs):
# begin iteration
pbar = tqdm(train_loader)
for b, data in enumerate(pbar):
# train step
outputs, losses = agent.train_func(data)
pbar.set_description("EPOCH[{}][{}]".format(e, b))
pbar.set_postfix({k: v.item() for k, v in losses.items()})
# validation step
if clock.step % cfg.val_frequency == 0:
data = next(val_loader)
outputs, losses = agent.val_func(data)
clock.tick()
clock.tock()
if clock.epoch % cfg.save_frequency == 0:
agent.save_ckpt()
# if clock.epoch % 10 == 0:
agent.save_ckpt('latest')
else:
# load trained weights
agent.load_ckpt(args.ckpt)
test_loader = get_dataloader('test', cfg)
save_dir = os.path.join(cfg.exp_dir, "results/fake_z_ckpt{}_num{}_pc".format(args.ckpt, args.n_samples))
if not os.path.exists(save_dir):
os.makedirs(save_dir)
all_zs = []
pbar = tqdm(test_loader)
cnt = 0
for i, data in enumerate(pbar):
with torch.no_grad():
pred_z, _ = agent.forward(data)
pred_z = pred_z.detach().cpu().numpy()
# print(pred_z.shape)
all_zs.append(pred_z)
pts = data['points'].detach().cpu().numpy()
for j in range(pred_z.shape[0]):
save_path = os.path.join(save_dir, "{}.ply".format(data['id'][j]))
write_ply(pts[j], save_path)
cnt += pred_z.shape[0]
if cnt > args.n_samples:
break
all_zs = np.concatenate(all_zs, axis=0)
# save generated z
save_path = os.path.join(cfg.exp_dir, "results/fake_z_ckpt{}_num{}.h5".format(args.ckpt, args.n_samples))
ensure_dir(os.path.dirname(save_path))
with h5py.File(save_path, 'w') as fp:
fp.create_dataset("zs", shape=all_zs.shape, data=all_zs)