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test-modified.py
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test-modified.py
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import os
import argparse
import numpy as np
import open3d as o3d
import torch
import torch.utils.data as Data
import tensorflow as tf
from models import PCN
from dataset import ShapeNet
from visualization import plot_pcd_one_view
from metrics.metric import l1_cd, l2_cd, emd, f_score
CATEGORIES_PCN = ['airplane', 'cabinet', 'car', 'chair', 'lamp', 'sofa', 'table', 'vessel']
CATEGORIES_PCN_NOVEL = ['new','bus', 'bed', 'bookshelf', 'bench', 'guitar', 'motorbike', 'skateboard', 'pistol']
def import_ply():
pcd_load=o3d.io.read_point_cloud('/home/gvc/Desktop/dev/PCN-PyTorch/data/PCNew/test_novel/01267162/4567.ply')
numpyarray=np.asarray(pcd_load.points)
tensor1=torch.from_numpy(numpyarray)
return tensor1
def make_dir(dir_path):
if not os.path.exists(dir_path):
os.makedirs(dir_path)
def export_ply(filename, points):
pc = o3d.geometry.PointCloud()
pc.points = o3d.utility.Vector3dVector(points)
o3d.io.write_point_cloud(filename, pc, write_ascii=True)
def test_single_category(category, model, params, save=True):
if save:
cat_dir = os.path.join(params.result_dir, category)
image_dir = os.path.join(cat_dir, 'image')
output_dir = os.path.join(cat_dir, 'output')
make_dir(cat_dir)
make_dir(image_dir)
make_dir(output_dir)
test_dataset = ShapeNet('/home/gvc/Desktop/dev/PCN-PyTorch/data/PCNew', 'test_novel' if params.novel else 'test', category)
test_dataloader = Data.DataLoader(test_dataset, batch_size=params.batch_size, shuffle=False)
index = 1
total_l1_cd, total_l2_cd, total_f_score = 0.0, 0.0, 0.0
with torch.no_grad():
for p, c in test_dataloader:
p = p.to(params.device)
c = c.to(params.device)
_, c_ = model(p)
exit()
total_l1_cd += l1_cd(c_, c).item()
total_l2_cd += l2_cd(c_, c).item()
for i in range(len(c)):
input_pc = p[i].detach().cpu().numpy()
output_pc = c_[i].detach().cpu().numpy()
gt_pc = c[i].detach().cpu().numpy()
total_f_score += f_score(output_pc, gt_pc)
if save:
plot_pcd_one_view(os.path.join(image_dir, '{:03d}.png'.format(index)), [input_pc, output_pc, gt_pc], ['Input', 'Output', 'GT'], xlim=(-0.35, 0.35), ylim=(-0.35, 0.35), zlim=(-0.35, 0.35))
export_ply(os.path.join(output_dir, '{:03d}.ply'.format(index)), output_pc)
index += 1
#avg_l1_cd = total_l1_cd / len(test_dataset)
#avg_l2_cd = total_l2_cd / len(test_dataset)
#avg_f_score = total_f_score / len(test_dataset)
return 0, 0, 0
def test(params, save=False):
if save:
make_dir(params.result_dir)
print(params.exp_name)
# load pretrained model
model = PCN(16384, 1024, 4).to(params.device)
model.load_state_dict(torch.load(params.ckpt_path))
model.eval()
print('\033[33m{:20s}{:20s}{:20s}{:20s}\033[0m'.format('Category', 'L1_CD(1e-3)', 'L2_CD(1e-4)', 'FScore-0.01(%)'))
print('\033[33m{:20s}{:20s}{:20s}{:20s}\033[0m'.format('--------', '-----------', '-----------', '--------------'))
if params.category == 'all':
if params.novel:
categories = CATEGORIES_PCN_NOVEL
else:
categories = CATEGORIES_PCN
l1_cds, l2_cds, fscores = list(), list(), list()
for category in categories:
avg_l1_cd, avg_l2_cd, avg_f_score = test_single_category(category, model, params, save)
print('{:20s}{:<20.4f}{:<20.4f}{:<20.4f}'.format(category.title(), 1e3 * avg_l1_cd, 1e4 * avg_l2_cd, 1e2 * avg_f_score))
l1_cds.append(avg_l1_cd)
l2_cds.append(avg_l2_cd)
fscores.append(avg_f_score)
print('\033[33m{:20s}{:20s}{:20s}{:20s}\033[0m'.format('--------', '-----------', '-----------', '--------------'))
print('\033[32m{:20s}{:<20.4f}{:<20.4f}{:<20.4f}\033[0m'.format('Average', np.mean(l1_cds) * 1e3, np.mean(l2_cds) * 1e4, np.mean(fscores) * 1e2))
else:
avg_l1_cd, avg_l2_cd, avg_f_score = test_single_category(params.category, model, params, save)
print('{:20s}{:<20.4f}{:<20.4f}{:<20.4f}'.format(params.category.title(), 1e3 * avg_l1_cd, 1e4 * avg_l2_cd, 1e2 * avg_f_score))
def test_single_category_emd(category, model, params):
test_dataset = ShapeNet('/home/gvc/Desktop/dev/PCN-PyTorch/data/PCNew', 'test_novel' if params.novel else 'test', category)
test_dataloader = Data.DataLoader(test_dataset, batch_size=params.batch_size, shuffle=False)
total_emd = 0.0
with torch.no_grad():
for p, c in test_dataloader:
p = p.to(params.device)
c = c.to(params.device)
_, c_ = model(p)
total_emd += emd(c_, c).item()
avg_emd = total_emd / len(test_dataset) / c_.shape[1]
return avg_emd
def test_emd(params):
print(params.exp_name)
# load pretrained model
model = PCN(16384, 1024, 4).to(params.device)
model.load_state_dict(torch.load(params.ckpt_path))
model.eval()
print('\033[33m{:20s}{:20s}\033[0m'.format('Category', 'EMD(1e-3)'))
print('\033[33m{:20s}{:20s}\033[0m'.format('--------', '---------'))
if params.category == 'all':
if params.novel:
categories = CATEGORIES_PCN_NOVEL
else:
categories = CATEGORIES_PCN
emds = list()
for category in categories:
avg_emd = test_single_category_emd(category, model, params)
print('{:20s}{:<20.4f}'.format(category.title(), 1e3 * avg_emd))
emds.append(avg_emd)
print('\033[33m{:20s}{:20s}\033[0m'.format('--------', '---------'))
print('\033[32m{:20s}{:<20.4f}\033[0m'.format('Average', np.mean(emds) * 1e3))
else:
avg_emd = test_single_category_emd(params.category, model, params)
print('{:20s}{:<20.4f}'.format(params.category.title(), 1e3 * avg_emd))
if __name__ == '__main__':
parser = argparse.ArgumentParser('Point Cloud Completion Testing')
parser.add_argument('--exp_name', type=str, help='Tag of experiment')
parser.add_argument('--result_dir', type=str, default='results', help='Results directory')
parser.add_argument('--ckpt_path', type=str, help='The path of pretrained model.')
parser.add_argument('--category', type=str, default='all', help='Category of point clouds')
parser.add_argument('--batch_size', type=int, default=1, help='Batch size for data loader')
parser.add_argument('--num_workers', type=int, default=6, help='Num workers for data loader')
parser.add_argument('--device', type=str, default='cuda:0', help='Device for testing')
parser.add_argument('--save', type=bool, default=False, help='Saving test result')
parser.add_argument('--novel', type=bool, default=False, help='unseen categories for testing')
parser.add_argument('--emd', type=bool, default=False, help='Whether evaluate emd')
params = parser.parse_args()
# pointcloud_file = "//home/gvc/Desktop/dev/PCN-PyTorch/data/PCN/test_novel/partial/02818832/1aa55867200ea789465e08d496c0420f.ply"
pointcloud_file = "/home/gvc/Desktop/dev/PCN-PyTorch/data/PCNew/test_novel"
test_dataloader = Data.DataLoader(pointcloud_file, batch_size=params.batch_size, shuffle=False)
for p, c in test_dataloader:
p = p.to(params.device)
print(p)
print(p.size())
exit()
all_points = o3d.io.read_point_cloud(pointcloud_file
)
xyz = np.array(all_points.points)
max_idx = np.argmax(xyz, axis=0)
xyz[:,0] = xyz[:,0,]/xyz[max_idx[0]][0]
xyz[:,1] = xyz[:,1]/xyz[max_idx[1]][1]
xyz[:,2] = xyz[:,2]/xyz[max_idx[2]][2]
print(xyz.shape)
pcd = o3d.geometry.PointCloud()
pcd.points = o3d.utility.Vector3dVector(xyz)
downpcd = pcd.voxel_down_sample(voxel_size=0.03)
downpcd_np = np.asarray(downpcd.points)
downpcd_tensor = torch.from_numpy(downpcd_np)
print(np.array(downpcd.points).shape)
model = PCN(16384, 1024, 4).to(params.device)
model.load_state_dict(torch.load(params.ckpt_path))
model.eval()
d=torch.unsqueeze(downpcd_tensor,dim=0)
d = d.to(params.device,dtype=torch.float)
_, c_ = model(d)
x = torch.squeeze(c_,dim=0)
result_numpy=x.detach().cpu().numpy()
final_pcd=o3d.geometry.PointCloud()
final_pcd.points=o3d.utility.Vector3dVector(result_numpy)
o3d.visualization.draw_geometries([final_pcd])
print(np.asarray(final_pcd.points))
o3d.io.write_point_cloud(filename="/home/gvc/Desktop/dev/PCN-PyTorch/test.ply", pointcloud=pcd, write_ascii=True)